Biology subjects

Anant, M.

Publications and source records attributed to Anant, M..

6 recordsLinked to original sources

bioRxiv2026

Generation and validation of an Acan-Cre mouse line to selectively label Class-B excitatory neurons of the cerebellar nuclei

The cerebellar nuclei form the main output structures of the cerebellum and are composed of a deeply conserved set of cell types. Two excitatory cell classes, Class-A and -B, are present in each cerebellar nucleus and mediate all excitatory output of the cerebellum. To provide genetic access to these cell types, here we identified Acan as a marker gene for Class-B cells and generated a knock-in Acan-P2A-Cre mouse line. We demonstrate that this Acan-Cre line selectively labels Class-B neurons in the cerebellar nuclei and validate its use in viral projection tracing. This new mouse line provides a valuable genetic tool to study cerebellar nuclei organization and function.

neuroscience↗

bioRxiv2025

Structural Validation of the Intermediate Leptomeningeal Layer in the Human Central Nervous System

Traditionally, the human central nervous system (CNS) is described as having three meningeal layers, from outer to inner: dura mater, arachnoid mater, and pia mater. The arachnoid and pia mater are called the leptomeninges, and the space between them is filled with cerebrospinal fluid (CSF). Using gross dissection, light microscopy, and ultrastructural analysis of fresh postmortem and cadaveric CNS specimens spanning fetal to adult ages (N=61), we demonstrate a fibrocellular intermediate leptomeningeal layer (ILL) from the cortex to the caudal end of the spinal cord. The ILL divides the subarachnoid space (SAS) into two distinct structural compartments, through which vessels and nerves pass. The ILL shows unique structural features, such as dips into the brains sulci and fissures, as a double-fold membrane that bears intra-layer trabeculae, carries vessels, and forms the perivascular sheath. Moreover, throughout the CNS, it appears to be a non-sieved barrier, characterized by the presence of tight and adherens junctions. ILL, predominantly in the spinal cord, contains macrophage-like cells, indicating its layer-specific immune properties. The ILL warrants recognition as a distinct human meningeal layer with potential barrier and immune functions. SignificanceO_ST_ABSAn Intermediate Leptomeningeal Layer encloses the Central Nervous System in HumansC_ST_ABSThe integrated analysis of our macroscopic, microscopic, and ultrastructural study provides robust support for an intermediate leptomeningeal layer (ILL) in the subarachnoid space (SAS) of the human central nervous system (CNS) along the entire neural axis. The ILL is a fibrocellular macroscopic structure, with restricted permeability, that divides the cerebrospinal fluid (CSF)-filled SAS into two distinct structural compartments. Uniquely, ILL revealed the presence of cells with macrophage-like properties, suggesting a possible role in immune surveillance. The ILL may redefine the established concept of protective coverings of CNS, CSF circulation dynamics, and the role of leptomeninges in health and disease, including drug delivery.

neuroscience↗

bioRxiv2023

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data

Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we developed CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we applied CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we applied CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.

bioinformatics↗

bioRxiv2023

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics

Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissues component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

bioinformatics↗

bioRxiv2023

Alignment of spatial transcriptomics data using diffeomorphic metric mapping

Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at https://github.com/JEFworks-Lab/STalign and as supplementary software with additional documentation and tutorials available at https://jef.works/STalign.

bioinformatics↗

bioRxiv2023

A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming

This paper explicates a solution to the problem of building correspondences between molecular-scale transcriptomics and tissue-scale atlases. The central model represents spatial transcriptomics as generalized functions encoding molecular position and high-dimensional transcriptomic-based (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling each atlas compartment as a homogeneous random field with unknown transcriptomic feature distribution. The algorithm presented solves simultaneously for the minimizing geodesic diffeomorphism of coordinates and latent atlas transcriptomic feature fractions by alternating LDDMM optimization for coordinate transformations and quadratic programming for the latent transcriptomic variables. We demonstrate the universality of the algorithm in mapping tissue atlases to gene-based and cell-based MERFISH datasets as well as to other tissue scale atlases. The joint estimation of diffeomorphisms and latent feature distributions allows integration of diverse molecular and cellular datasets into a single coordinate system and creates an avenue of comparison amongst atlas ontologies for continued future development.

bioengineering↗