Multispectral tracing in densely labeled mouse brain with nTracer.

SUMMARY: This note describes nTracer, an ImageJ plug-in for user-guided, semi-automated tracing of multispectral fluorescent tissue samples. This approach allows for rapid and accurate reconstruction of whole cell morphology of large neuronal populations in densely labeled brains. AVAILABILITY AND IMPLEMENTATION: nTracer was written as a plug-in for the open source image processing software ImageJ. The software, instructional documentation, tutorial videos, sample image and sample tracing results are available at https://www.cai-lab.org/ntracer-tutorial. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dimerization of mammalian kinesin-3 motors results in superprocessive motion.

The kinesin-3 family is one of the largest among the kinesin superfamily and its members play important roles in a wide range of cellular transport activities, yet the molecular mechanisms of kinesin-3 regulation and cargo transport are largely unknown. We performed a comprehensive analysis of mammalian kinesin-3 motors from three different subfamilies (KIF1, KIF13, and KIF16). Using Forster resonance energy transfer microscopy in live cells, we show for the first time to our knowledge that KIF16B motors undergo cargo-mediated dimerization. The molecular mechanisms that regulate the monomer-to-dimer transition center around the neck coil (NC) segment and its ability to undergo intramolecular interactions in the monomer state versus intermolecular interactions in the dimer state. Regulation of NC dimerization is unique to the kinesin-3 family and in the case of KIF13A and KIF13B requires the release of a proline-induced kink between the NC and subsequent coiled-coil 1 segments. We show that dimerization of kinesin-3 motors results in superprocessive motion, with average run lengths of ∼10 µm, and that this property is intrinsic to the dimeric kinesin-3 motor domain. This finding opens up studies on the mechanistic basis of motor processivity. Such high processivity has not been observed for any other motor protein and suggests that kinesin-3 motors are evolutionarily adapted to serve as the marathon runners of the cellular world.

Variability in training unlocks generalization in visual perceptual learning through invariant representations.

Stimulus and location specificity are long considered hallmarks of visual perceptual learning. This renders visual perceptual learning distinct from other forms of learning, where generalization can be more easily attained, and therefore unsuitable for practical applications, where generalization is key. Based on the hypotheses derived from the structure of the visual system, we test here whether stimulus variability can unlock generalization in perceptual learning. We train subjects in orientation discrimination, while we vary the amount of variability in a task-irrelevant feature, spatial frequency. We find that, independently of task difficulty, this manipulation enables generalization of learning to new stimuli and locations, while not negatively affecting the overall amount of learning on the task. We then use deep neural networks to investigate how variability unlocks generalization. We find that networks develop invariance to the task-irrelevant feature when trained with variable inputs. The degree of learned invariance strongly predicts generalization. A reliance on invariant representations can explain variability-induced generalization in visual perceptual learning. This suggests new targets for understanding the neural basis of perceptual learning in the higher-order visual cortex and presents an easy-to-implement modification of common training paradigms that may benefit practical applications.

TraceMontage: A method for merging multiple independent neuronal traces.

BACKGROUND: The ability to reconstruct neuronal networks, local microcircuits, or the entire connectome is a central goal of modern neuroscience. Recently, advancements in sample preparation (e.g., sample expansion and Brainbow labeling) and optical (e.g., confocal and light sheet) techniques have enabled the imaging of increasingly large neural systems with high contrast. Tracing neuronal structures from these images proves challenging, however, necessitating tools that integrate multiple neuronal traces, potentially derived by various methods, into one combined (montaged) result. NEW METHOD: Here, we present TraceMontage, an ImageJ/Fiji plugin for the combination of multiple neuron traces of a single image, either redundantly or non-redundantly. Internally, it uses graph theory to connect topological patterns in the 3-D spatial coordinates of neuronal trees. The software generates a single output tracing file containing the montage traces of the input tracing files and provides several measures of consistency analysis among multiple tracers. RESULTS AND COMPARISON TO EXISTING METHOD(S): To our knowledge, our software is the first dedicated method for the combination of tracing results. Combining multiple tracers increases the accuracy and speed of tracing of densely-labeled samples by harnessing collaborative effort. This utility is demonstrated using fluorescence microscope images from the hippocampus and primary visual cortex (V1) in Brainbow-labeled mice. CONCLUSIONS: TraceMontage provides researchers the ability to combine neuronal tracing data generated by either the same or different method(s). As datasets become larger, the ability to trace images in this parallel manner will help connectomics scale to increasingly larger neural systems.

Memory guidance of value-based decision making at an abstract level of representation.

Value-based decisions about alternatives we have never experienced can be guided by associations between current choice options and memories of prior reward. A critical question is how similar memories need to be to the current situation to effectively guide decisions. We address this question in the context of associative learning of faces using a sensory preconditioning paradigm. We find that memories of reward spread along established associations between faces to guide decision making. While memory guidance is specific for associated facial identities, it does not only occur for the specific images that were originally encountered. Instead, memory guidance generalizes across different images of the associated identities. This suggests that memory guidance does not rely on a pictorial format of representation but on a higher, view-invariant level of abstraction. Thus, memory guidance operates on a level of representation that neither over- nor underspecifies associative relationships in the context of obtaining reward.

A method for multiprotein assembly in cells reveals independent action of kinesins in complex.

Teams of processive molecular motors are critical for intracellular transport and organization, yet coordination between motors remains poorly understood. Here, we develop a system using protein components to generate assemblies of defined spacing and composition inside cells. This system is applicable to studying macromolecular complexes in the context of cell signaling, motility, and intracellular trafficking. We use the system to study the emergent behavior of kinesin motors in teams. We find that two kinesin motors in complex act independently (do not help or hinder each other) and can alternate their activities. For complexes containing a slow kinesin-1 and fast kinesin-3 motor, the slow motor dominates motility in vitro but the fast motor can dominate on certain subpopulations of microtubules in cells. Both motors showed dynamic interactions with the complex, suggesting that motor-cargo linkages are sensitive to forces applied by the motors. We conclude that kinesin motors in complex act independently in a manner regulated by the microtubule track.