Biology subjects

Misra, R. S.

Publications and source records attributed to Misra, R. S..

8 recordsLinked to original sources

bioRxiv2026

Reproducible-by-design: Romics Processor, a FAIR ecosystem for multi-omics and spatial-omics analysis

Multi-omics and spatial-omics technologies are exploding in use, producing increasingly complex datasets. Existing bioinformatics tools are developing rapidly but fail to fully enforce the FAIR principles, leaving the field vulnerable to escalating issues in computational reproducibility. Here, we introduce a reproducible-by-design paradigm represented in an omics data processing package, RomicsProcessor. At its core, the "Romics_object", which is a self-contained digital artifact that encapsulates the full history of the data from the original data to the fully processed state, capturing the details of the transformative steps and the required dependencies. This architecture ensures that computational workflows are fully portable and reproducible. In this manuscript, we demonstrate RomicProcessors computational capabilities and scalability on diverse datasets, including bulk proteomics, large-scale multiplexed immunofluorescence, and multi-batch mass spectrometry imaging. Providing a robust framework for truly FAIR Data Principles-based analysis, RomicsProcessor is a blueprint for the next generation of reproducible bioinformatics tools that can dramatically accelerate discovery in multi-omics biology in the era of artificial intelligence.

bioinformatics↗

bioRxiv2025

Exploring endothelial cell environments across organs in spatially resolved omics data

Endothelial cells are ubiquitously present in the human body and line the luminal surface of blood and lymphatic vessels. The oxygen-dependence of cells impacts their proximity to blood vessels, and consequently, to endothelial cells depending on their functional properties and priorities. This paper presents cell-to-nearest-endothelial-cell distance distributions for various cell types using 399 spatially resolved omics datasets from 14 studies comprising 12 tissue types with a total of 47,349,496 cells. Additionally, we developed an open-source web-based interactive tool, Cell Distance Explorer, that allows researchers to interactively visualize cell graphs and linkages in 2D and 3D datasets. Finally, we present a hierarchical neighborhood analysis focused on the endothelial cell neighborhoods in small and large intestine datasets. This paper provides an open-access resource (datasets, tools, and analyses) to characterize and compare cell distances and cell neighborhoods in spatially resolved omics data.

bioinformatics↗

bioRxiv2025

A Multimodal Spatial and Epigenomic Atlas of Human Adult Lung Topography

Developing high-resolution reference maps of disease-susceptible spatial niches is a critical step to mitigating the profound effects of lung disease. Here, we present an integrated multimodal single-nucleus human lung atlas (snHLA) profiling 746,047 nuclei from 49 mapped lung blocks spanning clinically relevant distal airways, alveoli, and interstitium across 11 healthy adults. Integrating snRNA-seq and SNARE-seq2, which co-assays chromatin accessibility and gene expression from the same nucleus, we resolved 70 molecularly distinct populations and captured 332,846 accessible chromatin regions, nominating new transcriptional regulators of human lung cell diversity. Spatial transcriptomics using MERFISH mapped 25 cell populations across 7 structural neighborhoods and multiplexed immunofluorescence localized cell subtypes and distal airway-defining protein markers, expanding and validating distinct lung structure-specific cell populations. This open access snHLA and companion Cell Type and Marker Gene Dictionary with anatomically aligned nomenclature delivers a foundational resource at an unprecedented resolution to interrogate the origins of lung pathophysiology.

cell biology↗

bioRxiv2025

Sorted-Cell Proteomics Reveals an AT1-Associated Epithelial Cornification Phenotype and Suggests Endothelial Redox Imbalance in Human Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a neonatal lung disease characterized by inflammation and scarring leading to long-term tissue damage. Previous whole tissue proteomics identified BPD-specific proteome changes and cell type shifts. Little is known about the proteome-level changes within specific cell populations in disease. Here, we sorted epithelial (EPI) and endothelial (ENDO) cell populations based on their differential surface markers from normal and BPD human lungs. Using a low-input compatible sample preparation method (MicroPOT), proteins were extracted and digested into peptides and subjected to Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) proteome analysis. Of the 4,970 proteins detected, 293 were modulated in abundance or detection in the EPI population and 422 were modulated in ENDO cells. Modulation of proteins associated with actin-cytoskeletal function such as SCEL, LMO7, and TBA1B were observed in the BPD EPIs. Using confocal imaging and analysis, we validated the presence of aberrant multilayer-like structures comprising SCEL and LMO7, known to be associated with epidermal cornification, in the human BPD lung. This is the first report of accumulation of cornification-associated proteins in BPD. Their localization in the alveolar parenchyma, primarily associated with alveolar type 1 (AT1) cells, suggests a role in the BPD post-injury response. In the ENDOs, redox balance and mitochondrial function pathways were modulated. Alternative mRNA splicing and cell proliferative functions were elevated in both populations suggesting potential dysregulation of cell progenitor fate. This study characterized the proteome of epithelial and endothelial cells from the BPD lung for the first time, identifying population-specific changes in BPD pathogenesis. New & NoteworthyThe study is the first to perform proteomics on sorted pulmonary epithelial and endothelial populations from BPD and age-matched control human donors. We identified an increase in cornification-associated proteins in BPD (e.g., SCEL and LMO7), and evidenced the presence of multilayered structures unique to BPD alveolar regions, associated with alveolar type 1 (AT1) cells. By changing the nature and/or biomechanical properties of the epithelium, these structures may alter the behavior of other alveolar cell types potentially contributing to the arrested alveolarization observed in BPD. Lastly, our data suggest the modulation of cell proliferation and redox homeostasis in BPD providing potential mechanisms for the reduced vascular growth associated with BPD.

biochemistry↗

bioRxiv2024

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRAs Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

bioinformatics↗

bioRxiv2022

Guided construction of single cell reference for human and mouse lung

Accurate cell type identification is a key and rate-limiting step in single cell data analysis. Single cell references with comprehensive cell types, reproducible and functional validated cell identities, and common nomenclatures are much needed by the research community to optimize automated cell type annotation and facilitate data integration, sharing, and collaboration. In the present study, we developed a novel computational pipeline to utilize the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples and constructed "LungMAP CellRef" and "LungMAP CellRef Seed" for both normal human and mouse lungs. "CellRef Seed" has an equivalent prediction power and produces consistent cell annotation as does "CellRef" but improves computational efficiency and simplifies its utilization for fast automated cell type annotation and online visualization. This atlas set incorporates 48 human and 40 mouse well-defined lung cell types catalogued from diverse anatomic locations and developmental time points. Using independent datasets, we demonstrated the utility of our CellRefs for automated cell type annotation analysis of both normal and disease lungs. User-friendly web interfaces were developed to support easy access and maximal utilization of the LungMAP CellRefs. LungMAP CellRefs are freely available to the pulmonary research community through fast interactive web interfaces to facilitate hypothesis generation, research discovery, and identification of cell type alterations in disease conditions.

bioinformatics↗

bioRxiv2020

Ontology-guided segmentation and object identification for developmental mouse lung immunofluorescent images

BackgroundImmunofluorescent confocal microscopy uses labeled antibodies as probes against specific macromolecules to discriminate between multiple cell types. For images of the developmental mouse lung, these cells are themselves organized into densely packed higher-level anatomical structures. These types of images can be challenging to segment automatically for several reasons, including the relevance of biomedical context, dependence on the specific set of probes used, prohibitive cost of generating labeled training data, as well as the complexity and dense packing of anatomical structures in the image. The use of an application ontology surmounts these challenges by combining image data with its metadata to provide a meaningful biological context, and hence constraining and simplifying the process of segmentation and object identification. ResultsWe propose an innovative approach for the automated analysis of complex and densely packed anatomical structures from immunofluorescent images that utilizes an application ontology to provide a simplified context for image segmentation and object identification. We describe how the logical organization of biological facts in the form of an ontology can provide useful constraints that enhance automatic processing of complex images. We demonstrate the results of ontology-guided segmentation and object identification in mouse developmental lung images from the Bioinformatics REsource ATlas for the Healthy lung (BREATH) database of the Molecular Atlas of Lung Development (LungMAP1) program. ConclusionThe microscopy analysis pipeline library (micap) is available at https://github.com/duke-lungmap-team/microscopy-analysis-pipeline. Code to reproduce our analysis of LungMAP images is also available at https://github.com/duke-lungmap-team/lungmap-pipeline. Finally, the application ontology is available at https://github.com/duke-lungmap-team/lung_ontology and includes example SPARQL queries. ContactAnna Maria Masci email: annamaria.masci@duke.edu

bioinformatics↗

bioRxiv2020

Single cell transcriptomic profiling identifies molecular phenotypes of newborn human lung cells

RationaleWhile animal model studies have extensively defined mechanisms controlling cell diversity in the developing mammalian lung, the limited data available from late stage human lung development represents a significant knowledge gap. The NHLBI Molecular Atlas of Lung Development Program (LungMAP) seeks to fill this gap by creating a structural, cellular and molecular atlas of the human and mouse lung. MethodsSingle cell RNA sequencing generated transcriptional profiles of 5500 cells obtained from two one-day old human lungs (born at gestational ages of 39 and 31 weeks) from the LungMAP Human Tissue Core Biorepository at the University of Rochester. Frozen single cell isolates were captured, and library preparation was completed on the Chromium 10X system. Data was analyzed in Seurat, and cellular annotation was performed using the ToppGene functional analysis tool. Single cell sequence data from 32000 postnatal day 1, 3, 7 and 10 mouse lung (n = 2 at each time point) cells generated by the LungMAP Research Center at Cincinnati Childrens Hospital and Medical Center, using Dropseq platform, was integrated with the human data. In situ hybridization was used to confirm the spatial location of cellular phenotypes. ResultsTranscriptional interrogation of donor newborn human lung cells identified distinct clusters representing multiple populations of epithelial, endothelial, fibroblasts, pericytes, smooth muscle, and immune cells and signature genes for each of these populations were identified. Computational integration of newborn human and postnatal mouse lung development cellular transcriptomes facilitated the identification of distinct cellular lineages among all the major cell types. Integration of the human and mouse cellular transcriptomes also demonstrated cell type-specific differences in developmental states of the newborn human lung cells. In particular, matrix fibroblasts could be separated into those representative of younger cells (n=393), or older cells (n=158). This is the first comprehensive molecular map of the cellular landscape of neonatal human lung, including biomarkers for cells at distinct states of development. Our results indicate that integrated single cell RNA profiling of human and mouse lung will help identify common and species-specific mechanisms of lung development and respiratory disease.

genomics↗