Biology subjects

Mu, S.

Publications and source records attributed to Mu, S..

At least 19 recordsLinked to original sources

bioRxiv2026

PRISM: Niche-informed Deciphering of Incomplete Spatial Multi-Omics Data

Spatial multi-omics data, characterizing the knowledge of diverse molecular layers, have become indispensable for the in situ analysis of tissue architecture and complex biological processes. Nevertheless, current spatial multi-omics sequencing protocols are often hindered by incompatible protocols, resulting in incomplete spatial multi-omics pairing due to inconsistent field-of-view or spatial resolution. To address this, we present PRISM, a novel computational method tailored for this scenario. PRISM leverages a niche-informed graph to propagate information from paired to unpaired regions, jointly achieving the spatial domain identification and spatial omics imputation. Extensive benchmarking on five diverse simulated and real experimental datasets demonstrated that PRISM outperformed existing methods in spatial multi-omics analysis tasks. Application to the human Parkinsons disease data revealed that PRISM accurately recovered dopamine-associated spatial domains and metabolite distributions masked by incomplete data gaps. PRISM offers a robust solution for bridging the integration gap inherent in incompatible sequencing protocols, thereby facilitating more accurate downstream biological interpretation.

bioinformatics↗

bioRxiv2025

Brain serotonin circuit reverses decline in physical activity with age

Physical inactivity increases with age and is the fourth leading risk factor for mortality. Establishing the mechanisms underpinning declining physical activity (PA) with age has remained both elusive and difficult to overcome. Older (50-70 years old) compared with younger (17-30 years old) UK participants showed reduced PA, and this profile was effectively modelled in mice. Analysis of brain serotonin (5-HT) neurons in the dorsal raphe (DR) revealed altered firing activity in older mice. Chemogenetically mimicking this 5-HTDR tone in young adult mice produced an older adult PA profile. Genetically blocking 5-HT activity at 5-HT2C receptors (5-HT2CRs) prevented the decline in PA with age. Importantly, barring 5-HT action at 5-HT2CRs specifically within the ventral tegmental area restored youthful PA levels and strength in older mice. These data fill a longstanding knowledge gap by defining brain circuitry programming the decline in PA with age, and importantly, a means to reverse it.

neuroscience↗

bioRxiv2025

Quantifying Uncertainty in Phasor-Based Time-Domain Fluorescence Lifetime Imaging Microscopy

The phasor approach to time-domain fluorescence lifetime imaging microscopy (FLIM) offers a powerful, fit-free method for analyzing complex fluorescence decay signals. However, its quantitative accuracy is fundamentally limited by noise--particularly photon shot noise--which introduces variability and bias in lifetime estimation and fluorophore unmixing. In this study, we present a theoretical uncertainty model for phasor-based time-domain FLIM that analytically captures the propagation of shot noise and quantifies its impact on phasor coordinates and fluorophore weight estimation. We validate the model using Monte Carlo simulations and experimental data acquired from standard fluorescent dyes and biological tissue samples. Our model improves the overall reliability and efficiency of phasor-based time-domain FLIM, particularly in photon-limited imaging applications.

bioinformatics↗

bioRxiv2024

Remote ischemic conditioning attenuates transneuronal degeneration and promotes stroke recovery via CD36-mediated efferocytosis.

BACKGROUNDRemote ischemic limb conditioning (RIC) has been implicated in cross-organ protection in cerebrovascular disease, including stroke. However, the lack of a consensus protocol and controversy over the clinical therapeutic outcomes of RIC suggest inadequate mechanistic understanding of RIC. The current study identifies RIC-induced molecular and cellular events in the blood that enhance long-term functional recovery in experimental cerebral ischemia METHODSNaive mice or mice subjected to transient ischemic stroke were randomly selected to receive sham conditioning or RIC in the hind limb at 2 h post-stroke. At 3d post-stroke, monocyte composition in the blood was analyzed, and brain tissue was examined for monocyte-derived macrophages (M{varphi}), levels of efferocytosis, and CD36 expression. Mouse with conditional deletion of CD36 in M{varphi} (cKOMM{varphi}) was used to establish the role of CD36 in RIC-mediated modulation of efferocytosis, transneuronal degeneration, and recovery following stroke. RESULTSRIC applied 2h after stroke increased entry of monocytes into the injured brain. In the post-ischemic brain, M{varphi} had increased levels of CD36 expression and efferocytosis. These changes in brain M{varphi} were derived from RIC-induced changes in circulating monocytes. In the blood, RIC increased CD36 expression in circulating monocytes and shifted monocytes to a proinflammatory LY6CHigh state. Conditional deletion of CD36 in M{varphi} abrogated the RIC-induced monocyte shift in the blood and efferocytosis in the brain. During the recovery phase of stroke, RIC rescued the loss of the volume and of tyrosine hydroxylase+ neurons in substantia nigra (SN) as well as behavioral deficits in WT mice, but not in cKOMM{varphi} mice. CONCLUSIONSRIC induces a shift in monocytes to a proinflammatory state with elevated CD36 levels, and this is associated with CD36-dependent efferocytosis in M{varphi}s that rescues delayed transneuronal degeneration in the post-ischemic brain and promotes stroke recovery. Together, these findings provide novel insight into our mechanistic understanding of how RIC improves in post-stroke recovery. Novelty and SignificanceO_ST_ABSWhat Is Known?C_ST_ABSO_LIInfiltrated monocyte-derived macrophages (M{varphi}) into the post-ischemic brain cause neural inflammation, but they also engage in efferocytosis that promotes tissue repair in the injured CNS. C_LIO_LIRemote ischemic limb conditioning (RIC) changes monocyte composition and enhances functional recovery in experimental brain ischemia. C_LIO_LIThe application of RIC is safe, feasible, and tolerable in stroke patients, but clinical outcomes remain inconsistent. C_LI What New Information Does This Article Contribute?O_LIWe provide experimental evidence that RIC modifies peripheral monocyte composition and molecular expression, and leads to favorable changes in debris clearance, structure integrity, transneuronal degeneration, and behavior following stroke. C_LIO_LIProtective effects of RIC disappear in the absence of CD36 in M{varphi}, suggesting an essential mechanistic role for CD36 in RIC-induced endogenous protective outcomes. C_LIO_LIThe current study demonstrates that immune-mediated RIC mechanisms facilitate inflammatory and recovery processes in the injured CNS. Given the challenges in directly manipulating the brain after stroke, the study suggests that RIC is a promising alternative strategy by inducing changes in peripheral monocytes that can influence injury progression and recovery. Moreover, RIC-induced peripheral changes uncovered by this study may serve as biomarkers to establish an optimal RIC protocol. C_LI

neuroscience↗

bioRxiv2024

QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The datas innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.

bioinformatics↗

bioRxiv2023

The Synaptic Architecture of Layer 5 Thick Tufted Excitatory Neurons in the Visual Cortex of Mice

The neocortex is one of the most critical structures that makes us human, and it is involved in a variety of cognitive functions from perception to sensory integration and motor control. Composed of repeated modules, or microcircuits, the neocortex relies on distinct cell types as its fundamental building blocks. Despite significant progress in characterizing these cell types 1-5, an understanding of the complete synaptic partners associated with individual excitatory cell types remain elusive. Here, we investigate the connectivity of arguably the most well recognized and studied excitatory neuron in the neocortex: the thick tufted layer 5 pyramidal cell 6-10 also known as extra telencephalic (ET) 11 neurons. Although the synaptic interactions of ET neurons have been extensively explored, a comprehensive characterization of their local connectivity remains lacking. To address this knowledge gap, we leveraged a 1 mm3 electron microscopic (EM) dataset. We found that ET neurons primarily establish connections with inhibitory cells in their immediate vicinity. However, when they extend their axons to other cortical regions, they tend to connect more with excitatory cells. We also find that the inhibitory cells targeted by ET neurons are a specific group of cell types, and they preferentially inhibit ET cells. Finally, we observed that the most common excitatory targets of ET neurons are layer 5 IT neurons and layer 6 pyramidal cells, whereas synapses with other ET neurons are not as common. These findings challenge current views of the connectivity of ET neurons and suggest a circuit design that involves local competition among ET neurons and collaboration with other types of excitatory cells. Our results also highlight a specific circuit pattern where a subclass of excitatory cells forms a network with specific inhibitory cell types, offering a framework for exploring the connectivity of other types of excitatory cells.

neuroscience↗

bioRxiv2023

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets ([~]1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVEs data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

neuroscience↗

bioRxiv2023

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5x107 chemical synapses between [~]130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

neuroscience↗

bioRxiv2023

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology 2, 3, electrophysiology 4, 5, transcriptomic expression 6-8, connectivity 9-13, or even a combination of such modalities 14-16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

neuroscience↗

bioRxiv2023

NEURD: A mesh decomposition framework forautomated proofreading and morphologicalanalysis of neuronal EM reconstructions

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

neuroscience↗

bioRxiv2023

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas -- including feedback connections -- suggesting the universality of the like-to-like connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting like-to-like connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

neuroscience↗

bioRxiv2023

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

neuroscience↗

bioRxiv2022

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

neuroscience↗

bioRxiv2022

CIRKO: A chemical-induced reversible gene knockout system for studying gene function in situ

Conditional loss and restoration of function are becoming important approaches for investigating gene function. Given that reversible conditional gene knockouts in cells required complicated manipulation, conditional inactivation and reactivation of a gene in primary somatic cells with limited proliferative capacity and in animal models remain difficult to achieve. Here, we first developed a reportable and reversible conditional intronic cassette (ReCOIN), wherein inactivation and reactivation of the gene are mediated via sequential expression of Cre and Flp recombinases, respectively. The expression pattern of the target gene can be monitored by direct visualization. To simply and tightly control temporal expression of the recombinases, on the basis of ReCOIN, we further presented a dual chemical-induced reversible gene knockout system (CIRKO) by insertion of reverse tetracycline transcriptional activator (rtTA) and tetracycline response element (TRE)-controlled Cre and FlpoERT2 recombinases cassettes into Rosa26 and Hipp11 loci of cells, respectively, in which transcription termination of the target gene can be induced at a specific stage in the presence of doxycycline, while gene restoration is achieved in the presence of doxycycline and tamoxifen simultaneously. This system provides a simple, rapid, and flexible gene switch for studying gene function in situ both in vitro and in vivo. Impact statementA novel chemical-induced reversible gene knockout system provides a simple, rapid, and flexible gene switch to facilitate the study of gene function in primary somatic cells in vitro, embryos in vitro or in vivo, and animals in vivo.

genetics↗

bioRxiv2022

Special Nuclear Layer Contacts Among Starburst Amacrine Cells in the Mouse Retina

We observed novel classes of cell-cell contacts between retinal starburst amacrine neurons, from finely detailed morphological reconstructions of cells from an electron microscopic image volume of a mouse retina. These contacts have peculiar morphological patterns and traits, different among the respective On and Off starburst amacrine subpopulations, but both occur within the soma layers as opposed to their regular laminae of contact within the inner plexiform layer.

neuroscience↗

bioRxiv2022

Quantitative Census of Local Somatic Features in Mouse Visual Cortex

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. It also presents a clear challenge for analysis to identify cell-types in data that contains tens of thousands of neurons, most of which have incomplete reconstructions. To address this challenge, we present the first systematic survey of the somatic region of all cells within a cubic millimeter of cortex using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to identify cell-types, including types defined primarily based on their connectivity patterns. We then describe how this classification facilitates cell type specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

neuroscience↗

bioRxiv2022

ABL1 Kinase plays an important role in spontaneous and chemotherapy-induced genomic instability in multiple myeloma

Genomic instability contributes to cancer progression and is at least partly due to dysregulated homologous recombination. Here, we show that an elevated level of ABL1 kinase overactivates the HR pathway and causes genomic instability in multiple myeloma (MM) cells. Inhibiting ABL1 with either shRNA or a pharmacological inhibitor (nilotinib) inhibits HR activity, reduces genomic instability, and slows MM cell growth. Moreover, inhibiting ABL1 rescues the HR dysregulation and genomic instability caused by melphalan, a chemotherapeutic agent used in MM treatment, and increases melphalans efficacy and cytotoxicity in vivo in a subcutaneous tumor model. In these tumors, nilotinib inhibits endogenous as well as melphalan-induced HR activity. These data demonstrate that inhibiting ABL1 using the clinically approved drug nilotinib reduces MM cell growth, promotes genome stability, increases the cytotoxicity of melphalan (and similar chemotherapeutic agents), and can potentially prevent or delay progression in MM patients.

cancer biology↗

bioRxiv2022

Apurinic/apyrimidinic nuclease 1 drives genomic evolution contributing to chemoresistance and tumorigenesis in solid tumor

Genomic instability fuels genomic alterations that befit cancer cells with necessary adaptations to keep proliferating and overcome the impact of host anti-tumor immunity and cytotoxic therapy. Since DNA breaks are required for genomic rearrangements to take place, we hypothesized that dysregulated nuclease activity mediates genomic instability in cancer. Using an integrated genomics protocol, we identified a four gene deoxyribonuclease signature correlating with genomic instability in six human cancers which included adenocarcinomas of esophagus (EAC), lung, prostate, stomach, pancreas and triple negative breast cancer. Functional screens confirmed the role of these nucleases in genomic instability and growth of cancer cells. Apurinic/apyrimidinic nuclease 1 (APE1), identified as top nuclease in functional screen, was further investigated in five cell lines representing four solid tumors (EAC, lung, prostate and breast cancer). We demonstrate that chemical as well as transgenic suppression of APE1 impaired growth/colony formation and increased cytotoxicity of chemotherapeutic agent, whereas inhibited spontaneous as well as chemotherapy-induced DNA breaks, homologous recombination (HR) activity and genomic instability in all cancer cell types tested. Treatment with APE1 inhibitor also impaired tumor growth and significantly increased efficacy of a chemotherapeutic agent in a subcutaneous mouse model of EAC. Overexpression of APE1 in normal esophageal epithelial cells increased DNA breaks and HR activity, leading to massive mutational, copy number as well as karyotypic instability. Evaluation of by whole genome sequencing identified HR as the top mutational process activated by APE1. Normal cells overexpressing APE1 grew as tumors in mice and tumors removed from mice displayed additional karyotypic changes, providing evidence of genomic instability in vivo. Overall, our data demonstrate that elevated APE1 dysregulates HR activity, G2/M checkpoint and genome stability thus contributing to tumorigenesis and chemoresistance in cancer. Therefore, inhibitors of APE1 have potential to inhibit growth and increase cytotoxicity of chemotherapeutic agents while minimizing spontaneous as well as chemotherapy-induced genomic damage and instability in EAC and other solid tumors.

cancer biology↗