Biology subjects

Jayakumar, J.

Publications and source records attributed to Jayakumar, J..

8 recordsLinked to original sources

bioRxiv2024

A three-dimensional histological cell atlas of the developing human brain

The human brain is believed to contain a full complement of neurons by the time of birth together with a substantial amount of the connectivity architecture, even though a significant amount of growth occurs postnatally. The developmental process leading to this outcome is not well understood in humans in comparison with model organisms. Previous magnetic resonance imaging (MRI) studies give three-dimensional coverage but not cellular resolution. In contrast, sparsely sampled histological or spatial omics analyses have provided cellular resolution but not dense whole brain coverage. To address the unmet need to provide a quantitative spatiotemporal map of developing human brain at cellular resolution, we leveraged tape-transfer assisted serial section histology to obtain contiguous histological series and unbiased imaging with dense coverage. Interleaved 20 thick Nissl and H&E series and MRI volumes are co-registered into multimodal reference volumes with 60 isotropic resolution, together with atlas annotations and a stereotactic coordinate system based on skull landmarks. The histological atlas volumes have significantly more contrast and texture than the MRI volumes. We computationally detect cells brain-wide to obtain quantitative characterization of the cytoarchitecture of the developing brain at 13-14 and 20-21 gestational weeks, providing the first comprehensive regional cell counts and characterizing the differential growth of the different brain compartments. Morphological characteristics permit segmentation of cell types from histology. We detected and quantified brain-wide distribution of mitotic figures representing dividing cells, providing an unprecedented spatiotemporal atlas of proliferative dynamics in the developing human brain. Further, we characterized the abundance and distribution of Cajal-Retzius cells, a transient cell population that plays essential roles in organizing glutamatergic cortical neurons into layers. Together, our study provides an unprecedented quantitative window into the developing human brain and the reference volumes and coordinate space should be useful for integrating spatial omics data sets with dense histological context.

neuroscience↗

bioRxiv2024

Establishing neuroanatomical correspondences across mouse and marmoset brain structures

Interest in the common marmoset is growing due to evolutionarily proximity to humans compared to laboratory mice, necessitating a comparison of mouse and marmoset brain architectures, including connectivity and cell type distributions. Creating an actionable comparative platform is challenging since these brains have distinct spatial organizations and expert neuroanatomists disagree. We propose a general theoretical framework to relate named atlas compartments across taxa and use it to establish a detailed correspondence between marmoset and mice brains. Contrary to conventional wisdom that brain structures may be easier to relate at higher levels of the atlas hierarchy, we find that finer parcellations at the leaf levels offer greater reconcilability despite naming discrepancies. Utilizing existing atlases and associated literature, we created a list of leaf-level structures for both species and establish five types of correspondence between them. One-to-one relations were found between 43% of the structures in mouse and 47% in marmoset, whereas 25% of mouse and 10% of marmoset structures were not relatable. The remaining structures show a set of more complex mappings which we quantify. Implementing this correspondence with volumetric atlases of the two species, we make available a computational tool for querying and visualizing relationships between the corresponding brains. Our findings provide a foundation for computational comparative analyses of mesoscale connectivity and cell type distributions in the laboratory mouse and the common marmoset.

neuroscience↗

bioRxiv2021

Spike independent replication of human coronavirus in bat cells

Bats are the reservoir for numerous human pathogens including coronaviruses. The factors leading to the emergence and sustained transmission of coronaviruses in humans are poorly understood. An outstanding question is how coronaviruses can accomplish a host switch with a likely mismatch between the surface protein spike of a bat virus and the human cellular receptor at the time of zoonotic virus transmission. To identify potential novel evolutionary pathways for zoonotic virus emergence, we serially passaged six human 229E isolates in a newly established Rhinolophus lepidus (horseshoe bat) kidney cells and analyzed viral genetic changes. Here we observed extensive deletions within the spike and ORF4 genes of five 229E viruses after passaging in bat cells. As a result, spike protein expression and infectivity of human cells was lost in 5 of 6 viruses but the capability to infect bat cells was maintained. Only viruses that expressed the spike protein could be neutralized by 229E spike-specific antibodies in human cells, whereas there was no neutralizing effect on viruses that do not express the spike protein inoculated on bat cells. However, one isolate acquired an early stop codon abrogating spike expression but maintaining infection in bat cells. Upon passaging this isolate in human cells, spike expression was restored due to acquisition of nucleotide insertions amongst virus subpopulations. Spike-independent infection of coronaviruses provides an alternative mechanism for viral maintenance in bats that does not rely on the compatibility of viral surface proteins and cellular entry receptors.

microbiology↗

bioRxiv2020

Detection and skeletonization of single neurons and tracer injections using topological methods

Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory. However, the tree-like shapes of neurons cannot be described easily as points in a vector space (the subtraction of two neuronal shapes is not a meaningful operation), and methods from computational topology are better suited to their analysis. Here we introduce methods from Discrete Morse (DM) Theory to extract the tree-skeletons of individual neurons from volumetric brain image data, and to summarize collections of neurons labelled by tracer injections. Since individual neurons are topologically trees, it is sensible to summarize the collection of neurons using a consensus tree-shape that provides a richer information summary than the traditional regional connectivity matrix approach. The conceptually elegant DM approach lacks hand-tuned parameters and captures global properties of the data as opposed to previous approaches which are inherently local. For individual skeletonization of sparsely labelled neurons we obtain substantial performance gains over state-of-the-art non-topological methods (over 10% improvements in precision and faster proofreading). The consensus-tree summary of tracer injections incorporates the regional connectivity matrix information, but in addition captures the collective collateral branching patterns of the set of neurons connected to the injection site, and provides a bridge between single-neuron morphology and tracer-injection data.

neuroscience↗

bioRxiv2020

Discovery of a 382-nt deletion during the early evolution of SARS-CoV-2

To date, the SARS-CoV-2 genome has been considered genetically more stable than SARS-CoV or MERS-CoV. Here we report a 382-nt deletion covering almost the entire open reading frame 8 (ORF8) of SARS-CoV-2 obtained from eight hospitalized patients in Singapore. The deletion also removes the ORF8 transcription-regulatory sequence (TRS), which in turn enhances the downstream transcription of the N gene. We also found that viruses with the deletion have been circulating for at least four weeks. During the SARS-CoV outbreak in 2003, a number of genetic variants were observed in the human population [1], and similar variation has since been observed across SARS-related CoVs in humans and bats. Overwhelmingly these viruses had mutations or deletions in ORF8, that have been associated with reduced replicative fitness of the virus [2]. This is also consistent with the observation that towards the end of the outbreak sequences obtained from human SARS cases possessed an ORF8 deletion that may be associated with host adaptation [1]. We therefore hypothesise that the major deletion revealed in this study may lead to an attenuated phenotype of SARS-CoV-2.

microbiology↗

bioRxiv2020

Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks

Understanding of neuronal circuitry at cellular resolution within the brain has relied on tract tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

neuroscience↗

bioRxiv2018

Diversity of feature selectivity in macaque visual cortex arising from limited number of broadly-tuned input channels

Spikes (action potential) responses of most primary visual cortical cells in the macaque are sharply tuned for the orientation of a line or an edge and neurons preferring similar orientations are clustered together in cortical columns. The preferred stimulus orientation of these columns span the full range of orientations, as observed in recordings of spikes, which represent the outputs of cortical neurons. However, when we imaged also the thalamic input to these cells that occur on a larger spatial scale, we found that the orientation domain map of the primary visual cortex did not show the diversity of orientations exhibited by signals representing outputs of the cells. This map was dominated by just the one orientation that is most commonly represented in subcortical responses. This supports cortical feature selectivity and columnar architecture being built upon feed-forward signals transmitted from the thalamus in a very limited number of broadly-tuned input channels.

neuroscience↗

bioRxiv2018

High precision automated detection of labelednuclei in terabyte-scale whole-brain volumetricimage data of mouse

There is a need in modern neuroscience for accurate and automated image processing techniques for analyzing the large volume of neuroanatomical data. For e.g., the use of light microscopy to image whole mouse brains in a mesoscopic scale produces individual neuroanatomical data volumes in the TerraByte range.. A fundamental task involves the detection and quantification of objects of a given type, e.g. neuronal nuclei or somata, in whole mouse brains. Traditionally this quantification is performed by human visual inspection with high accuracy, that is not scalable.. When state-of-the-art CNN and SVM-based methods are used to solve this classification problem, they achieve accuracy levels between 85 - 92%. However, higher rates of precision and recall, close to that of humans are necessary. In this paper, we describe an unsupervised, iterative algorithm, which provides a high close to human performance for a specific problem of broad interest, i.e. detection of Green Fluorescent Protein labeled nuclei in whole mouse brains. The algorithm judiciously combines classical computer vision (CV) techniques and is focused on the complex problem of decomposing strong overlapped objects (nuclei). Our proposed iterative method uses features detected on ridge lines over distance transformation and an arc based iterative spatial-filling method to solve the problem. We demonstrate our results on two whole mouse brain data sets of Gigabyte resolution and compare it with manual annotation of the brains. Our results show that an aptly designed CV algorithm with classical feature extractors when tailored to this problem of interest achieves near-ideal human-like performance. Quantitative analysis, when compared with the manually annotated ground truth, reveals that our approach performs better on whole mouse brain scans than general purpose machine learning (including deep CNN) methods.

neuroscience↗