Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Different subtypes of proprioceptors detect different kinematic features, such as joint position, movement, and vibration, but the mechanisms that underlie proprioceptor feature selectivity remain poorly understood. Using single-nucleus RNA sequencing (RNA-seq), we found that proprioceptor subtypes in the Drosophila leg lack differential expression of mechanosensitive ion channels. However, anatomical reconstruction of the proprioceptors and connected tendons revealed major biomechanical differences between subtypes. We built a model of the proprioceptors and tendons that identified a biomechanical mechanism for joint angle selectivity and predicted the existence of a topographic map of joint angle, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity in Drosophila. More broadly, the discovery of proprioceptive maps reveals common organizational principles between proprioception and other topographically organized sensory systems.

Liquid-liquid phase separation (LLPS) has emerged in recent years as an important physicochemical process for organizing diverse processes within cells via the formation of membraneless organelles termed biomolecular condensates. Emerging evidence now suggests that the formation and regulation of biomolecular condensates are also intricately linked to cancer formation and progression. We review the most recent literature linking the existence and/or dissolution of biomolecular condensates to different hallmarks of cancer formation and progression. We then discuss the opportunities that this condensate perspective provides for cancer research and the development of novel therapeutic approaches, including the perturbation of condensates by small-molecule inhibitors.

Liquid-liquid phase separation (LLPS) has emerged in recent years as an important physicochemical process for organizing diverse processes within cells via the formation of membraneless organelles termed biomolecular condensates. Emerging evidence now suggests that the formation and regulation of biomolecular condensates are also intricately linked to cancer formation and progression. We review the most recent literature linking the existence and/or dissolution of biomolecular condensates to different hallmarks of cancer formation and progression. We then discuss the opportunities that this condensate perspective provides for cancer research and the development of novel therapeutic approaches, including the perturbation of condensates by small-molecule inhibitors.

We investigate rules that govern neuronal arborization, speci%cally the local geometry of the

bifurcation of a neurite into its sub-branches. In the present study we set out to determine

the relationship between branch diameter and angle. Existing theories are based on minimizing a

neuronal volume cost function, or, alternatively, on the equilibrium of mechanical tension forces,

whichdepend on branchdiameters. Our experimental results utilizing two-dimensional cultured

neural networks partly corroborate both the volume optimization principles and the tension theory.

Deviation from pure tension forces equilibrium is explained by an additional force exerted by

the anchoring of the junction to the substrate.

The lobula giant movement detector (LGMD) is a large-field visual interneuron believed to be involved in collision avoidance and escape behaviors in orthopteran insects, such as locusts. Responses to approaching—or looming—stimuli are highly stereotypical, producing a peak that signals an angular size threshold. Over the past several decades, investigators have elucidated many of the mechanisms underpinning this response, demonstrating that the LGMD implements a multiplication in log-transformed coordinates. Furthermore, the LGMD possesses several mechanisms that preclude it responding to non-looming stimuli. This chapter explores these biophysical mechanisms, as well as highlighting insights the LGMD provides into general principles of dendritic integration.

We describe an approach to study the conformation of individual proteins during single particle tracking (SPT) in living cells. "Binder/tag" is based on incorporation of a 7-mer peptide (the tag) into a protein where its solvent exposure is controlled by protein conformation. Only upon exposure can the peptide specifically interact with a reporter protein (the binder). Thus, simple fluorescence localization reflects protein conformation. Through direct excitation of bright dyes, the trajectory and conformation of individual proteins can be followed. Simple protein engineering provides highly specific biosensors suitable for SPT and FRET. We describe tagSrc, tagFyn, tagSyk, tagFAK, and an orthogonal binder/tag pair. SPT showed slowly diffusing islands of activated Src within Src clusters and dynamics of activation in adhesions. Quantitative analysis and stochastic modeling revealed in vivo Src kinetics. The simplicity of binder/tag can provide access to diverse proteins.

The target for the "rapid" (<24 h) antidepressant effects of S-ketamine is unknown, vitiating programs to rationally develop more effective rapid antidepressants. To describe a drug's target, one must first understand the compartments entered by the drug, at all levels-the organ, the cell, and the organelle. We have, therefore, developed molecular tools to measure the subcellular, organellar pharmacokinetics of S-ketamine. The tools are genetically encoded intensity-based S-ketamine-sensing fluorescent reporters, iSKetSnFR1 and iSKetSnFR2. In solution, these biosensors respond to S-ketamine with a sensitivity, S-slope = delta(F/F)/(delta[S-ketamine]) of 0.23 and 1.9/μM, respectively. The iSKetSnFR2 construct allows measurements at <0.3 μM S-ketamine. The iSKetSnFR1 and iSKetSnFR2 biosensors display >100-fold selectivity over other ligands tested, including R-ketamine. We targeted each of the sensors to either the plasma membrane (PM) or the endoplasmic reticulum (ER). Measurements on these biosensors expressed in Neuro2a cells and in human dopaminergic neurons differentiated from induced pluripotent stem cells (iPSCs) show that S-ketamine enters the ER within a few seconds after appearing in the external solution near the PM, then leaves as rapidly after S-ketamine is removed from the extracellular solution. In cells, S-slopes for the ER and PM-targeted sensors differ by <2-fold, indicating that the ER [S-ketamine] is less than 2-fold different from the extracellular [S-ketamine]. Organelles represent potential compartments for the engagement of S-ketamine with its antidepressant target, and potential S-ketamine targets include organellar ion channels, receptors, and transporters.

Neurons derived from the same progenitor may acquire different fates according to their birth timing/order. To reveal temporally guided cell fates, we must determine neuron types as well as their lineage relationships and times of birth. Recent advances in genetic lineage analysis and fate mapping are facilitating such studies. For example, high-resolution lineage analysis can identify each sequentially derived neuron of a lineage and has revealed abrupt temporal identity changes in diverse Drosophila neuronal lineages. In addition, fate mapping of mouse neurons made from the same pool of precursors shows production of specific neuron types in specific temporal patterns. The tools used in these analyses are helping to further our understanding of the genetics of neuronal temporal identity.

The additive and synergistic therapeutic effects derived from combinations of chemotherapeutic drugs and radiation have established an indispensable paradigm: cancer must be attacked on multiple fronts. However, the increased antitumor efficacy of such combinational regimens is also associated with severe systemic toxicity, as these drugs cannot selectively target tumor cells. Monoclonal antibodies (mAbs), which have exquisite specificity for their antigens, are becoming an increasingly important class of antitumor agents, as they enhance the efficacy of various therapeutic regimens without significantly increasing systemic toxicity. Furthermore, preclinical and early clinical evidence indicate that combinations of antibody-based drugs provide even greater efficacy with minimal side effects. Unfortunately, the research, manufacturing and regulatory costs of mAb development pose a significant barrier to the use of antibody-based combination therapies. An emerging alternative is the use of dual-targeting bispecific antibodies (BsAbs). BsAbs are derived from the recombination of variable domains of two antibodies with different specificities; BsAbs are thus capable of binding both antigens of their parental antibodies. With the recent progress that has been made in antibody engineering technology, BsAbs that simultaneously target two tumor-associated molecules (eg, growth factor receptors) are being heralded for their potential to deliver two therapeutic moieties in a single molecule.

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.