Biology subjects

Rubin, A.

Publications and source records attributed to Rubin, A..

9 recordsLinked to original sources

bioRxiv2025

Mesoscale landscaping of the TRIM protein family reveals a novel human condensatopathy

The mesoscale organization of cells is central to cellular physiology and pathology. Cellular condensates often form via biomolecular phase separation, mediated by intrinsically disordered regions (IDRs) and represent a key mechanism for mesoscale organization. The TRI-partite Motif (TRIM) family of ubiquitin ligases is implicated in diverse cellular functions and disease, yet the role of biomolecular condensation in TRIM family organization remains understudied. Here, we systematically investigate the mesoscale localization of 72 TRIM proteins, revealing that a majority form condensates in distinct cellular compartments. IDR content correlates with dynamic condensate formation, suggesting a critical role in mesoscale organization. Focusing on TRIM8, associated with a neuro-renal disorder, we demonstrate that disease-causing truncations of the TRIM8 C-terminal IDR result in a condensatopathy, characterized by disrupted condensation, proteasomal regulation, and TAK1/NF{kappa}B signaling. Functional assays in cellular and animal models link these disruptions to podocyte dysfunction and impaired response to injury. Our findings establish a framework for understanding condensatopathies and the mesoscale principles governing TRIM family organization and function.

cell biology↗

bioRxiv2023

Differential encoding of odor and place in mouse piriform and entorhinal cortex

The integration of olfactory and spatial information is critical for guiding animal behavior. The lateral entorhinal cortex (LEC) is reciprocally interconnected with cortical areas for olfaction and the hippocampus and thus ideally positioned to encode odor-place associations. Here, we used mini-endoscopes to record neural activity in the mouse piriform cortex (PCx) and LEC. We show that in head-fixed mice, odor identity could be decoded from LEC ensembles, but less accurately than from PCx. In mice freely navigating a linear track, LEC ensemble activity at the odor ports was dominated by spatial information. Spatial position along the linear track could be decoded from LEC and PCx activity, however, PCx but not LEC exhibited strong behavior-driven modulation of positional information. Together, our data reveal that information about odor cues and spatial context is differentially encoded along the PCx-LEC axis. Significance statementFor most animals, the sense of smell is essential for successfully navigating the environment to find food, shelter, and mates. However, how olfactory and spatial information is integrated in the brain to support olfactory-guided behaviors remains poorly understood. In mammals, candidate brain regions thought to support odor-place associations include the olfactory (piriform, PCx) cortex, entorhinal cortex, and hippocampus. We here characterize the activity of cells in the PCx and lateral entorhinal cortex (LEC) of freely moving mice in response to odor cues presented in a linear track. Using mini-endoscope microscopy and population coding analyses, we find that information about odors, spatial location, and behavior is differentially encoded along the PCx-LEC axis.

neuroscience↗

bioRxiv2021

Hippocampal Astrocytes Encode Reward Location

Astrocytic calcium dynamics have been implicated in the encoding of sensory information, and modulating them has been shown to impact behavior. However, real-time calcium activity of astrocytes in the hippocampus of awake mice has never been investigated. We used 2-photon microscopy to chronically image CA1 astrocytes as mice ran in familiar or novel virtual environments and obtained water rewards. We found that astrocytes exhibit persistent ramping activity towards the reward location in a familiar environment, but not in a novel one. Using linear decoders, we could precisely predict the location of the mouse in a familiar environment from astrocyte activity alone. We could not do the same in the novel environment, suggesting astrocyte spatial activity is experience dependent. This is the first indication that astrocytes can encode location in spatial contexts, thereby extending their known computational capabilities, and their role in cognitive functions.

neuroscience↗

bioRxiv2021

Consistent population activity on the scale of minutes in the mouse hippocampus

Neurons in the hippocampus fire in consistent sequence over the timescale of seconds during the delay period of some memory experiments. For longer timescales, firing of hippocampal neurons also changes slowly over minutes within experimental sessions. It was thought that these slow dynamics are caused by stochastic drift or a continuous change in the representation of the episode, rather than consistent sequences unfolding over minutes. This paper studies the consistency of contextual drift in three chronic calcium imaging recordings from the hippocampus CA1 region in mice. Computational measures of consistency show reliable sequences within experimental trials at the scale of seconds as one would expect from time cells or place cells during the trial, as well as across experimental trials on the scale of minutes within a recording session. Consistent sequences in the hippocampus are observed over a wide range of time scales, from seconds to minutes. Hippocampal activity could reflect a scale-invariant spatiotemporal context as suggested by theories of memory from cognitive psychology.

neuroscience↗

bioRxiv2021

Single Cell Enhancer Activity Maps Neuronal Lineages in Embryonic Mouse Basal Ganglia

Enhancers integrate transcription factor signaling pathways that drive cell fate specification in the developing brain. We used single cell RNA-sequencing (scRNA-seq) to capture enhancer activity at single cell resolution and delineate specification of cells labeled by enhancers in mouse medial, lateral, and caudal ganglionic eminences (MGE, LGE, and CGE) at embryonic day (E)11.5. We combine enhancer-based reporter labeling with single-cell transcriptional readout to characterize enhancer activity and define cell populations in vivo. Seven enhancers had diverse activities in specific progenitor and neuronal populations within the GEs. We then applied enhancer-based labeling, scRNA-seq, and analysis of in situ hybridization (ISH) data to distinguish subtypes of MGE-derived GABAergic and cholinergic projection neurons and interneurons. This work demonstrates how the power of scRNA-seq can be extended by enhancer-based labelling and leveraging ISH data and reveals novel lineage specification paths underlying patterning of developing mouse brain.

neuroscience↗

bioRxiv2020

The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation

Transcription factors (TFs) bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multi-omic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep learning framework to model the cis-regulatory logic of chromatin accessibility. This identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach reveals the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code in dynamic gene regulation. HIGHLIGHTSO_LIAn integrative multi-omic resource profiling chromatin and expression dynamics across keratinocyte differentiation C_LIO_LIPredictive deep learning models of chromatin dynamics reveal a high-resolution cis-regulatory DNA motif lexicon of epidermal differentiation C_LIO_LIModel interpretation enables discovery of combinatorial cis-regulatory logic of homotypic and heterotypic motif combinations C_LIO_LIMassively parallel reporter experiments validate temporal dynamics and cis-regulatory logic of the combinatorial motif lexicon C_LI

genomics↗

bioRxiv2020

Representational drift in the mouse visual cortex

Neuronal representations in the hippocampus and related structures gradually change over time despite no changes in the environment or behavior. The extent to which such representational drift occurs in sensory cortical areas and whether the hierarchy of information flow across areas affects neural-code stability have remained elusive. Here, we address these questions by analyzing large-scale optical and electrophysiological recordings from six visual cortical areas in behaving mice that were repeatedly presented with the same natural movies. We found representational drift over timescales spanning minutes to days across multiple visual areas. The drift was driven mostly by changes in individual cells activity rates, while their tuning changed to a lesser extent. Despite these changes, the structure of relationships between the population activity patterns remained stable and stereotypic, allowing robust maintenance of information over time. Such population-level organization may underlie stable visual perception in the face of continuous changes in neuronal responses.

neuroscience↗

bioRxiv2019

Revealing neural correlates of behavior without behavioral measurements

Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a-priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition, and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an internal tuning-curve that characterizes its activity relative to the network activity, rather than relative to any pre-defined external variable - revealing place-tuning in the hippocampus and head-direction tuning in the thalamus and postsubiculum, without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the trajectory-phase. The structure of ensemble activity patterns was conserved across mice, allowing using one animals data to decode another animals behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

neuroscience↗

bioRxiv2019

Network-based hierarchical population structure analysis for large genomic datasets

Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic datasets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these datasets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We demonstrate the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrates how network-based approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic datasets.

genetics↗