Biology subjects

Hess, K.

Publications and source records attributed to Hess, K..

13 recordsLinked to original sources

bioRxiv2025

CocycleHunter: cohomology-based circular gene setenrichment and genetic phase estimation in single-cell RNA-seq data.

Standard single-cell RNA-seq analysis frameworks aggregate over-lapping biological processes and impose a single parametrization, conflating distinct programs. Here, we introduce a topological framework that detects and disentangles multiple cyclic processes directly from single-cell transcriptomic data. We validate this approach on synthetic datasets and scRNA-seq profiles of human dermal fibroblasts under control conditions and following androgen receptor (AR) silencing, as well as in vivo mouse prostate regeneration under androgen receptor add-back. We show robust cell cycle structure across conditions, identify an unbiased AR-linked stress signature related to the senescence and proliferation across organisms, and uncover cholesterol homeostasis as an AR-linked program in tissue regeneration. This framework enables identification and separation of concurrent cyclic processes from snapshot single-cell data, revealing complex multi-dimensional regulatory dynamics inaccessible to standard clustering analysis.

bioinformatics↗

bioRxiv2024

Efficiency and reliability in biological neural network architectures

Simplified models of neural networks have demonstrated the importance of establishing a reasonable tradeoff between memory capacity and fault-tolerance in cortical coding schemes. The intensity of the tradeoff is mediated by the level of neuronal variability. Indeed, increased redundancy in neuronal activity enhances the robustness of the code at the cost of the its efficiency. We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate this tradeoff, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity, by contrasting its higher-order structure to a random control. To confirm the relevance of our metric we analyzed several openly available connectomes, revealing that they all exhibited wider distributions of simplicial complexity across subpopulations than relevant controls. Using a biologically detailed cortical model and an electron microscopic data set of cortical connectivity with co-registered functional data, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neuronal networks, due to the heterogeneity of their connectivity. Our work thus suggests an avenue for resolving seemingly paradoxical previous results that assume homogeneous connectivity.

neuroscience↗

bioRxiv2023

Of mice and men: Increased dendritic complexity gives rise to unique human networks

The organizational principles that distinguish the human brain from other species have been a long-standing enigma in neuroscience. Focusing on the uniquely evolved human cortical layers 2 and 3, we computationally reconstruct the cortical architecture for mice and humans. We show that human pyramidal cells form highly complex networks, demonstrated by the increased number and simplex dimension compared to mice. This is surprising because human pyramidal cells are much sparser in the cortex. We show that the number and size of neurons fail to account for this increased network complexity, suggesting that another morphological property is a key determinant of network connectivity. Topological comparison of dendritic structure reveals much higher perisomatic (basal and oblique) branching density in human pyramidal cells. Using topological tools we quantitatively show that this neuronal structural property directly impacts network complexity, including the formation of a rich subnetwork structure. We conclude that greater dendritic complexity, a defining attribute of human L2 and 3 neurons, may provide the human cortex with enhanced computational capacity and cognitive flexibility. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/557170v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@c0adadorg.highwire.dtl.DTLVardef@1ca4aaeorg.highwire.dtl.DTLVardef@1ecf1b5org.highwire.dtl.DTLVardef@f0d44d_HPS_FORMAT_FIGEXP M_FIG C_FIG A. A multiscale analysis was performed to compare the mouse and human brains: from the anatomical properties of brain regions to the morphological details of single neurons. B. Human circuits are larger than mice in terms of size and number of neurons, but present decreased neuron density, resulting in increased distances between neurons, particularly among pyramidal cells. C. Greater network complexity emerges within the human brain. Network complexity is defined by larger groups of neurons forming complex interconnections throughout the network. D. The topological analysis of layer 2/3 pyramidal cells in the temporal cortex reveals an intriguing difference: human neurons exhibit a significantly larger number of dendritic branches, especially near the cell body compared to mice. This phenomenon is termed "higher topological complexity" in dendrites. Our findings suggest that dendritic complexity wields a more substantial influence on network complexity than neuron density.

neuroscience↗

bioRxiv2022

A machine learning liver-on-a-chip system for safer drug formulation

Drug metabolism leads to biotransformations of pharmaceutical substances that alter drug efficacy, toxicity, as well as drug interactions. Modeling these processes ex vivo stands to greatly accelerate our capacity to develop safe and efficacious drugs and formulations. Recognizing the liver as the primary site of drug metabolism, here we report a novel whole-tissue ex vivo liver screening platform that enabled modeling of hepatic metabolism and tracking of hepatotoxic drug metabolites. We applied the system for the characterization of acetaminophen (APAP) metabolism and identified interactions that can mitigate the generation of toxic metabolites ex vivo. Combining our experimental platform with state-of-the-art machine learning, we validated two novel functional excipients that can prevent APAP hepatotoxicity in vivo in mice. To assess translational potential, we prototyped a novel solid dosage form with controlled release of both APAP and our functional excipients. Our this platform provides innovative potential access to actionable data on drug metabolism to support the development of new therapeutic approaches.

bioengineering↗

bioRxiv2022

Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and a 211,712 neuron subvolume is made openly available to the community.

neuroscience↗

bioRxiv2021

Microglial MorphOMICs unravel region- and sex-dependent morphological phenotypes from postnatal development to degeneration

Microglia contribute to tissue homeostasis in physiological conditions with environmental cues influencing their ever-changing morphology. Strategies to identify these changes usually involve user-selected morphometric features, which, however, have proved ineffective in establishing a spectrum of context-dependent morphological phenotypes. Here, we have developed MorphOMICs, a topological data analysis approach to overcome feature-selection-based biases and biological variability. We extracted a spatially heterogeneous and sexually-dimorphic morphological phenotype for seven adult brain regions, with ovariectomized females forming their own distinct cluster. This sex-specific phenotype declines with maturation but increases over the disease trajectories in two neurodegeneration models, 5xFAD and CK-p25. Females show an earlier morphological shift in the immediately-affected brain regions. Finally, we demonstrate that both the primary- and the short terminal processes provide distinct insights to morphological phenotypes. MorphOMICs maps microglial morphology into a spectrum of cue-dependent phenotypes in a minimally-biased and semi-automatic way.

neuroscience↗

bioRxiv2021

Intact Drosophila Whole Brain Cellular Quantitation reveals Sexual Dimorphism

Establishing with precision the quantity and identity of the cell types of the brain is a prerequisite for a detailed compendium of gene and protein expression in the central nervous system. Currently however, strict quantitation of cell numbers has been achieved only for the nervous system of C.elegans. Here we describe the development of a synergistic pipeline of molecular genetic, imaging, and computational technologies designed to allow high-throughput, precise quantitation with cellular resolution of reporters of gene expression in intact whole tissues with complex cellular constitutions such as the brain. We have deployed the approach to determine with exactitude the number of functional neurons and glia in the entire intact larval Drosophila central nervous system (CNS), revealing fewer neurons and more glial cells than previously predicted. We also discover an unexpected divergence between the sexes at this juvenile developmental stage, with the female CNS having significantly more neurons than that of males. Topological analysis of our data establishes that this sexual dimorphism extends to deeper features of CNS organisation. We additionally extended our analysis to quantitate the expression of voltage-gated potassium channel family genes throughout the CNS and uncover substantial differences in abundance. Our methodology enables robust and accurate quantification of the number and positioning of cells within intact organs, facilitating sophisticated analysis of cellular identity, diversity, and gene expression characteristics.

neuroscience↗

bioRxiv2021

Architecture of the Neuro-Glia-Vascular System

Astrocytes connect the vasculature to neurons and mediate the supply of nutrients and biochemicals. They also remove metabolites from the neurons and extracellular environment. They are involved in a growing number of physiological and pathophysiological processes. Understanding the biophysical, physiological, and molecular interactions in this neuro-glia-vascular ensemble (NGV) and how they support brain function is severely restricted by the lack of detailed cytoarchitecture. To address this problem, we used data from multiple sources to create a data-driven digital reconstruction of the NGV at micrometer anatomical resolution. We reconstructed 0.2 mm3 of rat somatosensory cortical tissue with approximately 16000 morphologically detailed neurons, its microvasculature, and approximately 2500 morphologically detailed protoplasmic astrocytes. The consistency of the reconstruction with a wide array of experimental measurements allows novel predictions of the numbers and locations of astrocytes and astrocytic processes that support different types of neurons. This allows anatomical reconstruction of the spatial microdomains of astrocytes and their overlapping regions. The number and locations of end-feet connecting each astrocyte to the vasculature can be determined as well as the extent to which they cover the microvasculature. The structural analysis of the NGV circuit showed that astrocytic shape and numbers are constrained by vasculatures spatial occupancy and their functional role to form NGV connections. The digital reconstruction of the NGV is a resource that will enable a better understanding of the anatomical principles and geometric constraints which govern how astrocytes support brain function. Table of contentsO_ST_ABSMain pointsC_ST_ABSO_LIThe Blue Brain Project digitally reconstructs a part of neocortical Neuro-Glia-Vascular organization C_LIO_LIInterdependencies and topological methods allow dense in silico reconstruction from sparse experimental data C_LIO_LIThe polarized role of protoplasmic astrocytes constrains their shapes and numbers C_LI Table of contents image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/427241v3_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1260843org.highwire.dtl.DTLVardef@1e88718org.highwire.dtl.DTLVardef@fd66c5org.highwire.dtl.DTLVardef@6628a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Computational synthesis of cortical dendritic morphologies

Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles and investigating structural alterations associated with diseased brain states. Recently, we introduced a topological descriptor that reliably categorizes dendritic morphologies. We apply this descriptor to digitally synthesize dendrites to address the challenge of insufficient biological reconstructions. The synthesized cortical dendrites are statistically indistinguishable from the corresponding reconstructed dendrites in terms of morpho-electrical properties and connectivity. This topology-guided synthesis enables the rapid digital reconstruction of entire brain regions from relatively few reference cells, thereby allowing the investigation of links between neuronal morphologies and brain function across different spatio-temporal scales. We synthesized cortical networks based on structural alterations of dendrites associated with medical conditions and revealed principles linking branching properties to the structure of large-scale networks. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/040410v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@1ae790org.highwire.dtl.DTLVardef@58c3eborg.highwire.dtl.DTLVardef@1a6a9b1org.highwire.dtl.DTLVardef@ceae91_HPS_FORMAT_FIGEXP M_FIG Graphical abstractA topological model of neuronal shapes is used to investigate the link between the branching patterns of dendritic morphologies and the connectivity of the neuronal networks they form. Starting from reconstructed cells (in black) of cortical dendrites, we extract the topological barcode that is used to create a statistically similar synthesized pyramidal cell (in red), and respectively a group of pyramidal cells of the same morphological type. From reconstructed cells examples of all layers and morphological types we generate synthesized dendrites and build a synthesized cortical column (colors corresponds to cortical layers). The synthesized dedrites are statistically similar to the reconstructed dendrites in terms of morpho-electrical properties and the connectome of the synthesized column (colored connectome) is almost indistinguishable from the connectome of the reconstructed column (greyscale). C_FIG

neuroscience↗

bioRxiv2019

Computationally guided high-throughput design of self-assembling drug nanoparticles

Nanoformulations are transforming our capacity to effectively deliver and treat a myriad of conditions. However, many nanoformulation approaches still suffer from high production complexity and low drug loading. One potential solution relies on harnessing co-assembly of drugs and small molecular excipients to facilitate nanoparticle formation through solvent exchange without the need for chemical synthesis, generating nanoparticles with up to 95% drug loading. However, there is currently no understanding which of the millions of possible combinations of small molecules can result in the formation of these nanoparticles. Here we report the development of a high-throughput screening platform coupled to machine learning to enable the rapid evaluation of such nanoformulations. Our platform identified 101 novel self-assembling drug nanoparticles from 2.1 million pairings derived from 788 candidate drugs with one of 2686 excipients, spanning treatments for multiple diseases and often harnessing well-known food additives, vitamins, or approved drugs as carrier materials - with potential for accelerated approval and translation. Given their long-term stability and potential for clinical impact, we further characterize novel sorafenib-glycyrrhizin and terbinafine-taurocholic acid nanoparticles ex vivo and in vivo. We anticipate that this platform could accelerate the development of safer and more efficacious nanoformulations with high drug loadings for a wide range of therapeutics.

bioengineering↗

bioRxiv2018

The architecture of the endoplasmic reticulum is regulated by the reversible lipid modification of the shaping protein CLIMP-63

The endoplasmic reticulum (ER) has a complex morphology generated and maintained by membrane-shaping proteins and membrane energy minimization, though not much is known about how it is regulated. The architecture of this intracellular organelle is balanced between large, thin sheets that are densely packed in the perinuclear region and a connected network of branched, elongated tubules that extend throughout the cytoplasm. Sheet formation is known to involve the cytoskeleton-linking membrane protein 63 (CLIMP-63), though its regulation and the depth of its involvement remain unknown. Here we show that the post-translational modification of CLIMP-63 by the palmitoyltransferase ZDHHC6 controls the relative distribution of CLIMP-63 between the ER and the plasma membrane. By combining data-driven mathematical modeling, predictions, and experimental validation, we found that the attachment of a medium chain fatty acid, so-called S-palmitoylation, to the unique CLIMP-63 cytoplasmic cysteine residue drastically reduces its turnover rate, and thereby controls its abundance. Light microscopy and focused ion beam electron microcopy further revealed that enhanced CLIMP-63 palmitoylation leads to strong ER-sheet proliferation. Altogether, we show that ZDHHC6-mediated S-palmitoylation regulates the cellular localization of CLIMP-63, the morphology of the ER, and the interconversion of ER structural elements in mammalian cells through its action on the CLIMP-63 protein.\n\nSignificance StatementEukaryotic cells subcompartmentalize their various functions into organelles, the shape of each being specific and necessary for its proper role. However, how these shapes are generated and controlled is poorly understood. The endoplasmic reticulum is the largest membrane-bound intracellular compartment, accounting for more than 50% of all cellular membranes. We found that the shape and quantity of its sheet-like structures are controlled by a specific protein, cytoskeleton-linking membrane protein 63, through the acquisition of a lipid chain attached by an enzyme called ZDHHC6. Thus, by modifying the ZDHHC6 amounts, a cell can control the shape of its ER. The modeling and prediction technique used herein also provides a method for studying the interconnected function of other post-translational modifications in organelles.

cell biology↗

bioRxiv2018

Topological exploration of artificial neuronal network dynamics

One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechanics to describe the spatiotemporal structure of such network dynamics. Our novel approach employs tools from algebraic topology to characterize the global properties of network structure and dynamics.\n\nWe propose a method based on persistent homology to automatically classify network dynamics using topological features of spaces built from various spike-train distances. We investigate the efficacy of our method by simulating activity in three small artificial neural networks with different sets of parameters, giving rise to dynamics that can be classified into four regimes. We then compute three measures of spike train similarity and use persistent homology to extract topological features that are fundamentally different from those used in traditional methods. Our results show that a machine learning classifier trained on these features can accurately predict the regime of the network it was trained on and also generalize to other networks that were not presented during training. Moreover, we demonstrate that using features extracted from multiple spike-train distances systematically improves the performance of our method.

neuroscience↗

bioRxiv2018

Objective Classification of Neocortical Pyramidal Cells

A consensus on the number of morphologically different types of pyramidal cells (PCs) in the neocortex has not yet been reached, despite over a century of anatomical studies. This is because of a lack of agreement on the subjective classifications of neuron types, which is based on expert analyses of neuronal morphologies: the shapes of somata, dendrites, and axons. Even for neurons that are visually different to non-experts, there is no common ground to consistently distinguish morphological types. We found that objective classification is possible with methods from algebraic topology, and that the dendritic arbor is sufficient for reliable identification of distinct types of PCs. We also provide a solution for the more challenging problem of whether two similar neurons belong to different types or to a continuum of the same type. Using this scheme, we objectively identify seventeen types of PCs in the rat somatosensory cortex. Our topological classification does not require expert input, is stable, and helps settle the long-standing debate on whether cell-types are discrete or continuous morphological variations of each other.

neuroscience↗