Biology subjects

Sumbul, U.

Publications and source records attributed to Sumbul, U..

12 recordsLinked to original sources

bioRxiv2026

Discrete Morse Graph Construction for High-Dimensional Transcriptomic Data

Single-cell transcriptomic atlases resolve thousands of putative brain cell types, yet common analytical workflows often rely on low-dimensional embeddings that can distort neighborhood relationships and obscure continuous variation between closely related populations. We introduce single-cell Discrete Morse Graph Construction (scDMGC), a deterministic framework based on topological data analysis and discrete Morse theory that extracts a compact graph from neighborhood structure in high-dimensional gene-expression space. Because graph edges are derived from the original k-nearest-neighbor complex, scDMGC provides an interpretable multiscale representation of transcriptomic organization without requiring a low-dimensional embedding. Local maxima represent coherent transcriptional states, saddles quantify their separation, and gradient paths capture intermediate states and continuous transitions. Applied to cortical and hippocampal datasets, scDMGC recovers established inhibitory-neuron classes and elucidates transcriptional gradients. In mouse whole-cortex data, Morse graph structure evaluates cell-type hierarchy and type robustness. Persistence across scales provides a quantitative measure of cell-type identity and discrete versus continuous relationships. Finally, in Alzheimers disease single-nucleus data, scDMGC distinguishes changes in cell-type abundance from disease-associated shifts in transcriptional state. scDMGC therefore provides a multiscale framework for quantifying whether transcriptional populations form persistent cell types, how strongly external annotations are supported by intrinsic data structure, how cell types relate to one another, and where discrete organization transitions into continuous variation.

neuroscience↗

bioRxiv2026

Heterogeneity of white matter structure in the human brain

White-matter, which contains the long-range axons connecting different brain regions, makes up nearly half the human brain, yet the three-dimensional organization of individual axons has not been characterized. While advances in diffusion MRI have enabled macroscopic mapping of major WM pathways1, these methods are unable to resolve individual axons: their trajectories, density, and relative orientations2. Here, we present a histological and imaging pipeline optimized for post-mortem human white-matter that shows the 3D trajectories of densely stained large (diameter greater than ~1 m) projection axons. Applying our approach to multiple centimeter-scale WM regions in an adult human brain, we observed striking regional diversity in axonal organization. Specifically, we identified distinct architectural motifs ranging from loosely packed, multi-orientation meshworks (in most superficial white matter), to laminar lattice-like structures (near the basal ganglia), to tightly packed bundles of fibers (e.g. in the corpus callosum). We speculate that these patterns reflect local adaptations to spatial constraints, axonal density, and the diversity of axonal sources and targets, offering region-specific solutions to anatomical optimization problems. These findings offer new insights into the principles shaping brain connectivity and underscore the need for regionally detailed atlases of human white matter..

neuroscience↗

bioRxiv2025

Integrated Classification of Cortical Cells and Quantitative Projectomic Mapping Unveil Organizational Principles of Brain-Wide Connectomes at Single Cell Level

Molecularly defined cortical cell types have recently been linked to whole neuronal morphology (WNM), particularly those characterized by whole-brain-wide projections, such as intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) neurons. In contrast, classical morphological classifications (e.g., tufted TPC, small tufted SPC, and stellate SSC) are based primarily on local dendrosomatic and axonal structures, especially apical dendrites. This study bridges these perspectives by establishing a new neuronal taxonomy, analyzing the connectomes of defined cortical cell types, and comparing them with those obtained from bulk anterograde injections. Neurons were sparsely labeled via tamoxifen-inducible Cre lines with GFP reporters, and 1,419 WNM cells were comprehensively reconstructed with Vaa3D-TeraVR from [~]15 areas across six functional regions of molecularly labeled brains imaged with 2p-fMOST. These cells were newly classified by integrating current molecular-WNM and classical morphological perspectives, with sample size augmented by 1,455 publicly available WNM cells reconstructed from the Mouse-Light project and CEBSIT. This effort defined ten combined molecular-WNM-classical morphological cell types: L5ET_TPC, L6CT_NPC, L6b_HPC, and seven IT types--L2/3IT_TPC, L4IT_SSC, L4IT_UPC, L4IT_TPC, L5IT_SPC, L6IT_IPC, and L6IT_car3PC. Clustering, quantitative analyses and random Forest classifier objectively validated these types and revealed their distinct connectomes, along with convergent, topographic, and hierarchical organizations across their projection brain regions. At the single-cell level, multiple organizational principles governing cortico-cortical (C-C) and cortico-subcortical (C-subC) connectomes emerged with unprecedented detail, offering a precise GPS-like tool for in vivo recordings and robust datasets for neuronal network modeling. Comparisons with bulk anterograde injection data underscored the limitations of traditional methods in identifying projection targets. Overall, our approach provides significant insights into cortical circuitry and elucidates the complex interplay between neuronal molecular identity, whole morphology, and classical morphological classification.

neuroscience↗

bioRxiv2024

A scalable and modular computational pipeline for axonal connectomics: automated tracing and assembly of axons across serial sections

Progress in histological methods and in microscope technology has enabled dense staining and imaging of axons over large brain volumes, but tracing axons over such volumes requires new computational tools for 3D reconstruction of data acquired from serial sections. We have developed a computational pipeline for automated tracing and volume assembly of densely stained axons imaged over serial sections, which leverages machine learning-based segmentation to enable stitching and alignment with the axon traces themselves. We validated this segmentation-driven approach to volume assembly and alignment of individual axons over centimeter-scale serial sections and show the application of the output traces for analysis of local orientation and for proofreading over aligned volumes. The pipeline is scalable, and combined with recent advances in experimental approaches, should enable new studies of mesoscale connectivity and function over the whole human brain.

neuroscience↗

bioRxiv2024

VENTRAL HIPPOCAMPAL AND BASOLATERAL AMYGDALAR INPUTS TO DEEP, BUT NOT SUPERFICIAL LAYERS OF MEDIAL PREFRONTAL CORTEX UNDERLIE STRESS SUSCEPTIBILITY

Stress response is essential for adapting to an ever-changing environment. However, the mechanisms that render some individuals susceptible to stress are poorly understood. While chronic stress is known to induce dendritic atrophy and spine loss in medial prefrontal cortex (mPFC), its impact on synapses made by long-range projections terminating on the mPFC remains unknown. Here, we labeled synapses on male mouse mPFC dendrites formed by ventral hippocampus (VH), basolateral amygdala (BLA) and ventral tegmental area (VTA) long-range afferents using different-colored eGRASP constructs. We obtained multispectral 3D-images of the mPFC covering all cortical laminae, and automatically segmented the dendrites and synapses. In layer II/III, the relative abundances and spatial organizations of VH-mPFC and BLA-mPFC synapses changed similarly in stress resilient (SR) and stress susceptible (SS) mice when compared to stress naive (SN) mice. In layers Vb and VI, on the other hand, the percentage of BLA-mPFC increased and that of VH-mPFC decreased only in SS mice. Moreover, the distances of VH synapses to their corresponding closest BLA synapses decreased and the distances of BLA synapses to their corresponding closest VH synapses increased in the SS group. Consistently, the percentage of single dendritic segments receiving input from multiple brain regions increased in the SS group, suggesting that long-range synaptic inputs to deep layers of mPFC were disorganized in SS mice. Our findings demonstrate afferent- and lamina-specific differential reorganization of synapses between different stress phenotypes, suggesting specific roles for different long-range projections in mediating the stress response.

neuroscience↗

bioRxiv2023

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.

neuroscience↗

bioRxiv2022

Automated reconstruction of dendritic and axonal arbors reveals molecular correlates of neuroanatomy

AbstractNeuronal anatomy is central to the organization and function of brain cell types. However, anatomical variability within apparently homogeneous populations of cells can obscure such insights. Here, we report large-scale automation of neuronal morphology reconstruction and analysis on a dataset of 813 inhibitory neurons characterized using the Patch-seq method, which enables measurement of multiple properties from individual neurons, including local morphology and transcriptional signature. We demonstrate that these automated reconstructions can be used in the same manner as manual reconstructions to understand the relationship between some, but not all, cellular properties used to define cell types. We uncover gene expression correlates of laminar innervation on multiple transcriptomically defined neuronal subclasses and types. In particular, our results reveal correlates of the variability in Layer 1 (L1) axonal innervation in a transcriptomically defined subpopulation of Martinotti cells in the adult mouse neocortex.

neuroscience↗

bioRxiv2020

A solution to temporal credit assignment using cell-type-specific modulatory signals

Animals learn and form memories by jointly adjusting the efficacy of their synapses. How they efficiently solve the underlying temporal credit assignment problem remains elusive. Here, we re-analyze the mathematical basis of gradient descent learning in recurrent spiking neural networks (RSNNs) in light of the recent single-cell transcriptomic evidence for cell-type-specific local neuropeptide signaling in the cortex. Our normative theory posits an important role for the notion of neuronal cell types and local diffusive communication by enabling biologically plausible and efficient weight update. While obeying fundamental biological constraints, including separating excitatory vs inhibitory cell types and observing connection sparsity, we trained RSNNs for temporal credit assignment tasks spanning seconds and observed that the inclusion of local modulatory signaling improved learning efficiency. Our learning rule puts forth a novel form of interaction between modulatory signals and synaptic transmission. Moreover, it suggests a computationally efficient learning method for bio-inspired artificial intelligence.

neuroscience↗

bioRxiv2020

Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types

Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined. HighlightsO_LIGeneration and evaluation of more than 130 million single-cell models with active conductances along the reconstructed morphology faithfully recapitulate the electrophysiology of 230 in vitro experiments. C_LIO_LIOptimized ion channel conductances along the cellular morphology ( all-active) are characteristic of model complexity and offer enhanced biophysical realism. C_LIO_LIIon channel conductance vectors of all-active models classify transcriptomically defined cell-types. C_LIO_LICell type differences in ion channel conductances predicted by the models correlate with experimentally measured single-cell gene expression differences in inhibitory (Pvalb, Sst, Htr3a) and excitatory (Nr5a1, Rbp4) classes. C_LIO_LIA set of ion channel conductances identified by comparing between cell type model populations explain electrophysiology differences between these types in simulations and brain slice experiments. C_LIO_LIAll-active models recapitulate multimodal properties of excitatory and inhibitory cell types offering a systematic and causal way of linking differences between them. C_LI

neuroscience↗

bioRxiv2019

Transcriptomic evidence for dense peptidergic neuromodulation networks in mouse cortex

Seeking new insights into the homeostasis, modulation and plasticity of cortical synaptic networks, we have analyzed results from a single-cell RNA-seq study of 22,439 mouse neocortical neurons. Our analysis exposes transcriptomic evidence for dozens of molecularly distinct neuropeptidergic modulatory networks that directly interconnect all cortical neurons. This evidence begins with a discovery that transcripts of one or more neuropeptide precursor (NPP) and one or more neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes are highly abundant in all, or very nearly all, cortical neurons. Individual neurons express diverse subsets of NP signaling genes from palettes encoding 18 NPPs and 29 NP-GPCRs. These 47 genes comprise 37 cognate NPP/NP-GPCR pairs, implying the likelihood of local neuropeptide signaling. Here we use neuron-type-specific patterns of NP gene expression to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.\n\nImpactSingle-cell mRNA sequencing data from mouse neocortex expose evidence for peptidergic neuromodulatory networks that locally interconnect every cortical neuron\n\nData HighlightsO_LIAt least 97% of mouse neocortical neurons express one or more of 18 neuropeptide precursor proteins (NPP) genes at very high levels\nC_LIO_LIAt least 98% of cortical neurons express one or more of 29 neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes cognate to the 18 highly expressed NPP genes\nC_LIO_LINeocortical expression of these 18 NPP and 29 NP-GPCR genes is highly neuron-type-specific and their expression patterns differentiate transcriptomic neuron types with exceptional power\nC_LIO_LINeuron-type taxonomy and type-specific expression of 37 cognate NPP / NP-GPCR gene pairs generate testable predictions of at least 37 local intracortical neuromodulation networks\nC_LI

neuroscience↗

bioRxiv2018

The Markov link method: a nonparametric approach to combine observations from multiple experiments

This paper studies measurement linkage. An example from cell biology helps explain the problem: imagine for a given cell we can either sequence the cells RNA or we can examine its morphology, but not both. Given a cells morphology, what do we expect to see in its RNA? Given a cells RNA, what do we expect in its morphology? More broadly, given a measurement of one type, can we predict measurements of the other type? This measurement linkage problem arises in many scientific and technological fields. To solve this problem, we develop a nonparametric approach we dub the \"Markov link method\" (MLM). The MLM makes a conditional independence assumption that holds in many multi-measurement contexts and provides a way to estimate the link, the conditional probability of one type of measurement given the other. We derive conditions under which the MLM estimator is consistent and we use simulated data to show that it provides accurate measures of uncertainty. We evaluate the MLM on real data generated by a pair of single-cell RNA sequencing techniques. The MLM characterizes the link between them and helps connect the two notions of cell type derived from each technique. Further, the MLM reveals that some aspects of the link cannot be determined from the available data, and suggests new experiments that would allow for better estimates.\n\nSignificance StatementNovel experimental techniques are developing quickly, and each technique gives new perspectives. Ideally we would build theories that account for many perspectives at once. This is not easy. One challenge is that many experiments use measurement techniques that alter or destroy the subject, making it impossible to measure the same subject with both techniques and difficult to combine data from different experiments. In this paper we develop the Markov Link Method, a new tool that overcomes this challenge.

bioinformatics↗

bioRxiv2018

Whole-neuron synaptic mapping reveals local balance between excitatory and inhibitory synapse organization

The balance between excitatory and inhibitory (E and I) synaptic inputs is thought to be critical for information processing in neural circuits. However, little is known about the principles of spatial organization of E and I synapses across the entire dendritic tree of mammalian neurons. We developed a new, open-source, reconstruction platform for mapping the size and spatial distribution of E and I synapses received by individual, genetically-labeled, layer 2/3 cortical pyramidal neurons (PNs) in vivo. We mapped over 90,000 E and I synapses across twelve L2/3 PNs and uncovered structured organization of E and I synapses across dendritic domains as well as within individual dendritic segments in these cells. Despite significant, domain-specific, variations in the absolute density of E and I synapses, their ratio is strikingly balanced locally across dendritic segments. Computational modeling indicates that this spatially-precise E/I balance dampens dendritic voltage fluctuations and strongly impacts neuronal firing output.

neuroscience↗