Biology subjects

Schwartz, G.

Publications and source records attributed to Schwartz, G..

13 recordsLinked to original sources

bioRxiv2026

Eyewire II - A connectomic resource for resolving cell types and circuits of the mouse retina

Comprehensive wiring diagrams from electron microscopy (EM) are a powerful tool to understand the inner workings of the brain. The retina is an easily accessible part of the brain that performs complex visual computations. Its thin, layered structure offers a unique opportunity to decipher neural cell types and map their connectivity. A major obstacle has been the limited size of existing retinal EM datasets, which could not resolve rare cell types and neurons with large dendritic arbors. Here, we describe Eyewire II, a large-scale EM dataset covering nearly 1 mm2 of the adult mouse retina - roughly 10-100 times larger than previous retinal EM volumes. Human proofreading of an automated reconstruction has so far yielded more than 8, 000 bipolar cells, 13, 000 amacrine cells, and 4, 000 retinal ganglion cells. Automated detection is complete for synaptic ribbons and in progress for conventional synapses. Prior to EM imaging, visual responses to diverse stimuli - including natural movies - were recorded in a subset of neurons using two-photon Ca2+ imaging, enabling direct alignment of morphological and functional cell type identity. To enable high-throughput, automatic cell typing, we devised a human-in-the-loop approach that combines deep learning with human expert annotations. As a proof-of-principle, we show that morphological features of reconstructed bipolar cells are sufficient to recover all 15 known bipolar cell types with regular, non-overlapping mosaics. Together, these data and tools establish Eyewire II as a shared resource for the field of retina research. Already now, more than 30 laboratories worldwide are contributing proofreading, expert annotations, and software tools, advancing Eyewire II towards a complete cell type catalog and synaptic wiring diagram of a mammalian retina.

neuroscience↗

bioRxiv2025

Hyperdominance and rarity in Amazonian secondary forests

Recent studies have shown that a few tree species dominate abundance and carbon storage in undisturbed Amazonian forests. However, this pattern remains poorly understood in forests regrowing after deforestation. Understanding dominance patterns in secondary forests is important as it could improve insights into their functioning and guide management and restoration efforts. We used unweighted and novel plot-weighted approaches to assess hyperdominance in abundance and carbon storage of trees and palms during secondary forest recovery, using data from 102 plots across four regions in the eastern Amazon, separated by an average distance of 600 km (range: 150-1,000 km). Secondary forests show extreme hyperdominance in all approaches, with just 16-25 species ([~]3.6-5.6%) accounting for over 50% of abundance and carbon storage. A small subset of species ([~]9%) were found across all regions and successional stages, highlighting their key role in ecosystem structure and function during forest regrowth. Annona exsucca, Cecropia palmata, Tapirira guianensis, Inga alba, Attalea maripa, Amphiodon effusus, Banara guianensis, and Vismia guianensis consistently emerged as hyperdominant across stem size classes, approaches, and metrics, jointly accounting for roughly 16-27% of total abundance and carbon in Amazonian secondary forests. For larger stems >10cm DBH, hyperdominant species exhibited lower wood density compared to non-hyperdominant species across all successional stages, suggesting these specialist pioneers have distinct ecological strategies from the rest of the community. Our findings indicate that secondary forest functioning can be largely predicted by a relatively limited set of species, providing a framework for future research ranging from ecophysiology to remote sensing of canopy traits and restoration planning.

ecology↗

bioRxiv2025

Early neuronal reprogramming and cell cycle reentry shape Alzheimer's disease progression

Alzheimers disease (AD) is a progressive neurodegenerative disorder characterized by hallmark pathologies, synaptic dysfunction, neuronal loss, gliosis and cognitive decline-dementia. Recent large-scale cell atlases of human AD brains exposed vulnerability of specific neuronal subtypes and highlighted early, coordinated glial responses, suggesting glial involvement in disease progression. However, the timing and nature of neuronal changes, differences between neuronal subtypes, and their coordination with glia remain unclear. Here, we used non-negative matrix factorization to identify co-expression gene programs in single-nucleus RNA profiles from the prefrontal cortex of 437 samples from donors whose clinical symptoms ranged between no cognitive impairment and AD dementia. This approach identified early coordinated transcriptional changes across all neuronal subtypes, preceding clinical symptoms of cognitive decline, and validated in independent snRNA-seq, proteomics, and ELISA datasets. We found neurons in AD undergo rapid modulation of synaptic genes, accompanied by convergence of neurons into two distinct programs: An oxidative stress and apoptosis program abundant in vulnerable neuronal subtypes, and a DNA damage and cell-cycle reentry program associated with resilient subtypes. Moreover, neuronal reprogramming was closely tied to glial responses, and diverged between AD to non-AD brain aging, suggesting neuro-glial coordinated reprogramming shapes the AD cascade and influences disease outcomes.

neuroscience↗

bioRxiv2024

Hippocampus oxytocin signaling promotes prosocial eating in rats

The hypothalamic neuropeptide oxytocin (OT) influences both food intake and social behavior. Given that food preference and consumption are heavily affected by social factors in mammals, it is critical to understand the extent that OTs role in regulating these two fundamental behaviors is interconnected. Here we evaluated the role of OT signaling in the dentate gyrus of the dorsal hippocampus (HPCd), a brain region recently linked with eating and social memory, on food preference and consumption in rats under conditions that vary with regards to social presence and conspecific familiarity. Results from neuropharmacological and virogenetic knockdown approaches reveal that HPCd OT signaling promotes eating in the presence of a familiar but not an unfamiliar conspecific. Additionally, HPCd OT receptor signaling is required for the social transmission of food preference. These findings collectively identify the HPCd as a novel substrate where oxytocin synergistically influences eating and social behaviors.

neuroscience↗

bioRxiv2023

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

bioinformatics↗

bioRxiv2022

A presynaptic source drives differing levels of surround suppression in two mouse retina ganglion cell types

In early sensory systems, cell-type diversity generally increases from the periphery into the brain, resulting in a greater heterogeneity of responses to the same stimuli. Surround suppression is a canonical visual computation that begins within the retina and is found at varying levels across retinal ganglion cell types. Our results show that divergence in the level of surround suppression occurs subcellularly, at bipolar cell synapses. Using single-cell electrophysiology and serial block-face scanning electron microscopy, we show that two retinal ganglion cell types exhibit very different levels of surround suppression even though they receive input from the same set of bipolar cell types. This divergence of the bipolar cell signal occurs through synapse-specific regulation by amacrine cells at the scale of tens of microns. These findings indicate that each synapse of a single bipolar cell can carry a unique visual signal, expanding the number of possible functional channels at the earliest stages of visual processing.

neuroscience↗

bioRxiv2022

IRIS: Integrated Retinal Functionality in Image Sensors

Neuromorphic image sensors draw inspiration from the biological retina to implement visual computations in electronic hardware. Gain control in phototransduction and temporal differentiation at the first retinal synapse inspired the first generation of neuromorphic sensors, but processing in downstream retinal circuits, much of which has been discovered in the past decade, has not been implemented in image sensor technology. We present a technology-circuit co-design solution that implements two motion computations occurring at the output of the retina that could have wide applications for vision based decision making in dynamic environments. Our simulations on Globalfoundries 22nm technology node show that, by taking advantage of the recent advances in semiconductor chip stacking technology, the proposed retina-inspired circuits can be fabricated on image sensing platforms in existing semiconductor foundries. Integrated Retinal Functionality in Image Sensors (IRIS) technology could drive advances in machine vision applications that demand robust, high-speed, energy-efficient and low-bandwidth real-time decision making.

bioengineering↗

bioRxiv2021

Differences in spike generation instead of synaptic inputs determine the feature selectivity of two retinal cell types.

The output of spiking neurons depends both on their synaptic inputs and on their intrinsic properties. Retinal ganglion cells (RGCs), the spiking projection neurons of the retina, comprise over 40 different types in mice and other mammals, each tuned to different features of visual scenes. The circuits providing synaptic input to different RGC types to drive feature selectivity have been studied extensively, but there has been substantially less research aimed at understanding how the intrinsic properties of RGCs differ and how those differences impact feature selectivity. Here, we introduce an RGC type in the mouse, the Bursty Suppressed-by-Contrast (bSbC) RGC, whose contrast selectivity is shaped by its intrinsic properties. Surprisingly, when we compare the bSbC RGC to the OFF sustained alpha (OFFsA) RGC that receives similar synaptic input, we find that the two RGC types exhibit starkly different responses to an identical stimulus. We identified spike generation as the key intrinsic property behind this functional difference; the bSbC RGC undergoes depolarization block in conditions where the OFFsA RGC maintains a high spike rate. Pharmacological experiments, imaging, and compartment modeling demonstrate that these differences in spike generation are the result of differences in voltage-gated sodium channel conductances. Our results demonstrate that differences in intrinsic properties allow these two RGC types to detect and relay distinct features of an identical visual stimulus to the brain.

neuroscience↗

bioRxiv2021

Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression

Classification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We used visually-evoked responses to classify 1859 mouse RGCs into 42 types. We also obtained morphological or transcriptomic data from subsets and used these measurements to align the functional classification to publicly available morphological and transcriptomic data sets. We created an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.

neuroscience↗

bioRxiv2020

Automated Edge-Aware Refinement of Anatomical Atlases in 3D and Its Application to the Developing Mouse Brain

3D imaging data necessitate 3D reference atlases for accurate quantitative interpretation. Existing computational methods to generate 3D atlases from 2D-derived atlases result in extensive artifacts, while manual curation approaches are labor-intensive. We present a computational approach for 3D atlas construction that substantially reduces artifacts by identifying anatomical boundaries in the underlying imaging data and using these to guide 3D transformation. Anatomical boundaries also allow extension of atlases to complete edge regions. Applying these methods to the eight developmental stages in the Allen Developing Mouse Brain Atlas (ADMBA) led to more comprehensive and accurate atlases. We generated imaging data from fifteen whole mouse brains to validate atlas performance and observed qualitative and quantitative improvement (37% greater alignment between atlas and anatomical boundaries). We provide the methods as the MagellanMapper software and the eight 3D reconstructed ADMBA atlases. These resources facilitate whole-organ quantitative analysis between samples and across development.

bioinformatics↗

bioRxiv2019

Oncogenic Notch promotes long-range regulatory interactions within hyperconnected 3D cliques

Chromatin loops enable transcription factor-bound distal enhancers to interact with their target promoters to regulate transcriptional programs. Although developmental transcription factors, such as active forms of Notch, can directly stimulate transcription by activating enhancers, the effect of their oncogenic subversion on the 3-dimensional (3D) organization of the cancer genome is largely undetermined. By mapping chromatin looping genome-wide in Notch-dependent triple-negative breast cancer and B-cell lymphoma, we show that far beyond the well-characterized role of Notch as an activator of distal enhancers, Notch regulates its direct target genes through establishing new long-range regulatory interactions. Moreover, a large fraction of Notch-promoted regulatory loops forms highly interacting enhancer and promoter spatial clusters, termed "3D cliques". Loss-and gain-of-function experiments show that Notch preferentially targets hyperconnected 3D cliques that regulate the expression of crucial proto-oncogenes. Our observations suggest that oncogenic hijacking of developmental transcription factors can dysregulate transcription through widespread effects on the spatial organization of cancer genomes.

cancer biology↗

bioRxiv2018

Novel genes for autism implicate both excitatory and inhibitory cell lineages in risk

We present the largest exome sequencing study to date focused on rare variation in autism spectrum disorder (ASD) (n=35,584). Integrating de novo and case-control variation with an enhanced Bayesian framework incorporating evolutionary constraint against mutation, we implicate 99 genes in ASD risk at a false discovery rate (FDR) [≤] 0.1. Of these 99 risk genes, 46 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 50 show higher frequencies in individuals ascertained for ASD, and comparing ASD cases with disruptive mutations in the two groups shows differences in phenotypic presentation. Expressed early in brain development, most of the risk genes have roles in neuronal communication or regulation of gene expression, and 12 fall within recurrent copy number variant loci. In human cortex single-cell gene expression data, expression of the 99 risk genes is also enriched in both excitatory and inhibitory neuronal lineages, implying that disruption of these genes alters the development of both neuron types. Together, these insights broaden our understanding of the neurobiology of ASD.

genetics↗

bioRxiv2017

mTORC1 in AGRP neurons integrates exteroceptive and interoceptive food-related cues in the modulation of adaptive energy expenditure in mice

Energy dissipation through interscapular brown adipose tissue (iBAT) thermogenesis is an important contributor to adaptive energy expenditure. However, it remains unresolved how acute and chronic changes in energy availability are detected by the brain to adjust iBAT activity and maintain energy homeostasis. Here we provide evidence that AGRP inhibitory tone to iBAT represents an energy-sparing circuit that integrates environmental food cues and internal signals of energy availability. We establish a role for the nutrient-sensing mTORC1 signaling pathway within AGRP neurons in the detection of environmental food cues and internal signals of energy availability, and in the bi-directional control of iBAT thermogenesis during nutrient deficiency and excess. Collectively, our findings provide insights into how mTORC1 signaling within AGRP neurons surveys energy availability to engage iBAT thermogenesis, and identify AGRP neurons as a neuronal substrate for the coordination of energy intake and adaptive expenditure under varying physiological and environmental contexts.

neuroscience↗