Primary cilia are microtubule-based sensory organelles that have been conserved throughout eukaryotic evolution. As discussed in this Review, a cilium is an elongated and highly specialized structure, and, together with its ability to selectively traffic and concentrate proteins, lipids and second messengers, it creates a signaling environment distinct from the cell body. Ciliary signaling pathways adopt a bow-tie network architecture, in which diverse inputs converge on shared effectors and second messengers before diverging to multiple outputs. Unlike other cellular bow-tie systems, cells exploit ciliary geometry, compartmentalization and infrastructure to enhance sensitivity at multiple scales, from individual molecular reactions to entire signaling pathways. In cilia, integration of the bow-tie network architecture with their specialized structure and unique environment confers robustness and evolvability, which enables cilia to acquire diverse signaling roles. However, this versatility comes with vulnerability - rare mutations that disrupt the features most essential for cilia robustness cause multisystem ciliopathies.

Genetically-encoded fluorescent biosensors have revolutionized our understanding of complex systems by permitting the in situ observation of chemical activities. However, only a comparatively small set of chemical activities can be monitored, largely due to the need to identify protein domains that undergo conformational and/or association changes in response to a stimulus. Here, we present a strategy that can convert ’simple’ affinity binders such as nanobodies into biosensors for their innate targets by introducing a peptide sequence that competes for the binding site. We demonstrate proof-of-concept implementations of this ’NanoBlock’ design, developing sensors based on the ALFA nanobody and on the PDZ domain of Erbin. We show that these sensors can reliably detect their targets in vitro, in mammalian cells, and as part of fluorescence-activated cell sorting (FACS) experiments. In doing so, our strategy offers a way to strongly expand the range of cellular processes that can be probed using fluorescent biosensors.

Enzyme-based self-labeling tags enable the covalent attachment of synthetic molecules to proteins inside living cells. A frontier of this field is designing cell-permeable multifunctional ligands that contain fluorophores in combination with affinity tags or pharmacological agents. This is challenging since attachment of additional chemical moieties onto fluorescent ligands can adversely affect membrane permeability. To address this problem, we examined the chemical properties of rhodamine-based self-labeling tag ligands through the lens of medicinal chemistry. We found that the lactone-zwitterion equilibrium constant () of rhodamines inversely correlates with their distribution coefficients (log), suggesting that ligands based on dyes exhibiting low and high log values, such as Si-rhodamines, would efficiently enter cells. We designed cell-permeable multifunctional HaloTag ligands with a biotin moiety to purify mitochondria or a JQ1 appendage to translocate BRD4 within the nucleus. We found that translocation of BRD4 to constitutive heterochromatin in cells leads to apparent increases in transcriptional activity. These fluorescent reagents enable affinity capture and translocation of intracellular proteins in living cells, and our general design concepts will facilitate the design of multifunctional chemical tools for biology.

Preprint: https://doi.org/10.1101/2022.07.02.498544 
Preprint: https://doi.org/10.32388/0xcyuc 

Solar flares, email exchanges, and many natural or social systems exhibit bursty dynamics, with periods of intense activity separated by long inactivity. These patterns often follow power- law distributions in inter-event intervals or event rates. Existing models typically capture only one of these features and rely on non-local memory, which complicates analysis and mechanistic interpretation. We introduce a novel self-reinforcing point process whose event rates are governed by local, Markovian nonlinear dynamics and post-event resets. The model generates power-law tails for both inter-event intervals and event rates over a broad range of exponents observed empirically across natural and human phenomena. Compared to non-local models such as Hawkes processes, our approach is mechanistically simpler, highly analytically tractable, and also easier to simulate. We provide methods for model fitting and validation, establishing this framework as a versatile foundation for the study of bursty phenomena.

Signaling pathways induce stereotyped transcriptional changes as stem cells progress into mature cell types during embryogenesis. Signaling perturbations are necessary to discover which genes are responsive or insensitive to pathway activity. However, gene regulation is additionally dependent on cell state-specific factors like chromatin modifications or transcription factor binding. Thus, transcriptional profiles need to be assayed in single cells to identify potentially multiple, distinct perturbation responses among heterogeneous cell states in an embryo. In perturbation studies, comparing heterogeneous transcriptional states among experimental conditions often requires samples to be collected over multiple independent experiments, which can introduce confounding batch effects. We present Design-Aware Integration of Single Cell ExpEriments (DAISEE), a new algorithm that models perturbation responses in single-cell datasets collected according to complex experimental designs. We demonstrate that DAISEE improves upon a previously available integrative nonnegative matrix factorization framework, more efficiently separating perturbation responses from confounding variation. We use DAISEE to integrate newly collected single-cell RNA sequencing datasets from 5-h-old zebrafish embryos expressing optimized photoswitchable MEK (psMEK), which globally activates the extracellular signal-regulated kinase (ERK), a signaling molecule involved in many cell specification events. psMEK drives some cells that are normally not exposed to ERK signals toward other wild type states and induces novel states expressing early-acting endothelial genes. Overactive signaling is therefore capable of producing unexpected gene expression states in developing embryos.

bioRxiv preprint: https://www.doi.org/10.1101/2024.09.05.610903

Optical nanoscopy of intact biological specimens has been transformed by recent advancements in hydrogel-based tissue clearing and expansion, enabling the imaging of cellular and subcellular structures with molecular contrast. However, existing high-resolution fluorescence microscopes are physically limited by objective-to-specimen distance, which prevents the study of whole-mount specimens without physical sectioning. To address this challenge, we developed a photochemical strategy for spatially precise sectioning of specimens. By combining serial photochemical sectioning with lattice light-sheet imaging and petabyte-scale computation, we imaged and reconstructed axons and myelin sheaths across entire mouse olfactory bulbs at nanoscale resolution. An olfactory bulb–wide analysis of myelinated and unmyelinated axons revealed distinctive patterns of axon degeneration and de-/dysmyelination in the neurodegenerative brain, highlighting the potential for peta- to exabyte-scale super-resolution studies using this approach. High-resolution microscopes have a short working distance, making it difficult to see deep within large biological samples such as an intact brain. Slicing the tissue with a blade can reach deeper, but this often distorts or destroys the fine structures that scientists want to study. By embedding a sample in a light-sensitive hydrogel, Wang et al. demonstrated a gentler approach using a precise ray or sheet of light to dissolve or cut away tissue layer by layer. After each layer is removed, the newly exposed surface is imaged, allowing for a complete, high-resolution, three-dimensional reconstruction without damaging physical contact. 

bioRxiv preprint: https://www.biorxiv.org/content/10.1101/2024.08.01.605857v1

Neural mechanisms underlying sexually dimorphic social behaviors remain enigmatic in most species. In Drosophila, sexually dimorphic P1/pC1x neurons have been described as a site of sensory integration that regulates mating and aggressive behaviors. We show that the male P1/pC1x population forms a highly intertwined network with male-specific mAL and aSP-a neurons that is poised to regulate male behavior. The 48 P1/pC1x cell types exhibit heterogeneous synaptic connections with a subset receiving strong input from identified sensory pathways. We also describe circuit motifs by which P1 and sexually dimorphic aIPg neurons co-regulate social behaviors. Genetic driver lines for these cell types were generated and used to discover distinct roles for P1/pC1x cell types in promoting social acoustic signaling and male-male interactions. Our results reveal unexpected diversity in the connectivity and behavioral roles of the P1/pC1x cell types and provide essential genetic tools for interrogating their neurophysiological and behavioral functions.

Power-law scaling in coarse-grained data suggests critical dynamics, but the true source of this scaling often remains unclear. Here, we analyze neural activity recorded during spatial navigation, reproducing power-law scaling under a phenomenological renormalization group (PRG) procedure that clusters units by activity similarity. Such scaling was previously linked to criticality. Here, we show that the iterative nature of the procedure itself leads to the emergence of power laws when applied to heterogeneous, non-interacting units obeying spatially structured activity without requiring critical interactions. Furthermore, the scaling exponents produced by heteregeneous non-interacting units match the observed exponents in recorded neural data. A simplified version of the PRG further reveals how heterogeneity smooths transitions across scales, mimicking critical behavior. The resulting exponents depend systematically on system and population size, predictions confirmed by subsampling the data.

Researchers have long noted the differences in synapse count between different EM reconstructions of similar circuitry. In this paper we attempt to determine the portion of these differences that may be due to different sample preparation and imaging techniques, in particular serial-section transmission imaging (SS-TEM) compared to focused ion beam with scanning electron microscopy (FIB-SEM). To do this, we compare synapse detection in the major Drosophila EM reconstructions - FANC, MANC, FAFB (with original and new synapses), male CNS, BANC, and HemiBrain, plus several smaller reconstructions. We look at raw synapse counts to avoid any dependence on proofreading, and compensate insofar as possible for the confounds of sample sizes differences and different software detection efficiency. The result are estimates, per compartment and for the sample as a whole, of the number of synapses that would be visible to a skilled human observer. These are then compared across all samples, using regions which are reconstructed in common for each sample pair. We find that in almost all known cases where a volume has been reconstructed by both techniques, isotropic FIB-SEM reconstructions show more human-visible synapses than microtome sliced reconstructions, typically by more than 40%. This strongly suggests, but does not conclusively prove, that synapses are easier to see in isotropic FIB-SEM data.

fMRI signals were traditionally seen as slow and sampled in the order of seconds, but recent technological advances have enabled much faster sampling rates. We hypothesized that high-frequency fMRI signals can capture spontaneous neural activity that index brain states. Using fast fMRI (TR=378ms) and simultaneous EEG in 27 humans drifting between sleep and wakefulness, we found that fMRI spectral power increased during NREM sleep (compared to wakefulness) across several frequency ranges as fast as 1Hz. This fast fMRI power was correlated with canonical arousal-linked EEG rhythms (alpha and delta), with spatiotemporal correlation patterns for each rhythm reflecting a combination of shared arousal dynamics and rhythm-specific neural signatures. Using machine learning, we found that alpha and delta EEG rhythms can be decoded from fast fMRI signals, in subjects held-out from the training set, showing that fMRI as fast as 0.9Hz (alpha) and 0.7Hz (delta) contains reliable neurally-coupled information that generalizes across individuals. Finally, we demonstrate that this fast fMRI acquisition allows for EEG rhythms to be decoded from 3.8s windows of fMRI data. These results reveal that high-frequency fMRI signals are coupled to dynamically varying brain states, and that fast fMRI sampling allows for more temporally precise quantification of spontaneous neural activity than previously thought possible.