Biology subjects

Shrestha, H. K.

Publications and source records attributed to Shrestha, H. K..

6 recordsLinked to original sources

bioRxiv2026

MakeMyFigure: An Interactive Platform for Reproducible Quantitative Data Visualization, Analysis, and Scientific Figure Construction

Reproducible data analysis and visualization are essential for reliable biological and biomedical research, yet creating high-quality, publication-ready figures often requires moving data and results between multiple analysis, visualization, and graphics tools. This fragmented, multi-layered process can make it difficult to trace how individual figure panels were generated, preserve the underlying analytical decisions, and reproduce them later. Here, we introduce MakeMyFigure, a free and open-source platform for data visualization, analysis, and creation of multi-panel scientific figures. MakeMyFigure integrates data processing, statistical analysis, visualization, and figure assembly within a single workflow. It supports a range of quantitative data formats and structures, including feature-by-sample matrices and precomputed statistical results, and provides data-aware visualization recommendations to help users select appropriate plots from a library of 38 visualization types. These capabilities allow researchers to move directly from experimental measurements to commonly used statistical analyses and graphical representations without requiring programming expertise. Eighteen statistical procedures are implemented, and their results are drawn directly onto the plot families that support statistical annotation, keeping analytical results linked to the panels they generate. Importantly, MakeMyFigure uses machine-readable JSON specifications to record the identity and checksum of the source data, the processing steps, visualization settings, and statistical parameters used to generate each figure panel. It can also save a figure as a portable package, freezing the specifications together with the data. This allows figures to be regenerated from their recorded specifications and source data, or from the package alone on another computer, rather than relying on manually reconstructed workflows. Using published datasets from several biological fields, independent statistical validation in R, and a comparison with 15 representative analysis, visualization, and figure-generation tools, we show that MakeMyFigure combines accessible, code-free figure creation with panel-level computational reproducibility. Overall, MakeMyFigure provides a unified approach for creating, documenting, and reproducing scientific figures. Source code and documentation are available at https://github.com/surPoudel/make-my-figure, https://github.com/surPoudel/make-my-figure/releases/tag/v1.1.0.

bioinformatics↗

bioRxiv2026

Single Plaque Proteomics Reveals the Composition and Dynamics of the Amyloid Microenvironment in Alzheimer's Disease

Alzheimers disease (AD) is characterized by amyloid plaques that form complex microenvironments in the brain. However, the molecular composition of these plaques and their temporal regulation are not well defined. Here, we developed a sensitive workflow for quantitative proteomic profiling of single plaques using refined laser capture microdissection and data-independent acquisition mass spectrometry (LCM-DIA-MS). From >200 plaques and control regions in AD mouse models (5xFAD and APP-KI) and human brains, we quantified >7,000 proteins, revealing stage-dependent, cell-type-related remodeling of the amyloid proteome (amyloidome). Temporal profiling uncovered early immune and lysosomal activation followed by engagement of RNA processing and synaptic pathways. Cross-model and cross-species analyses determined a conserved amyloidome including APOE, MDK, PTN, and HTRA1, validated by co-localization in imaging analysis. Network analysis highlighted modules in lipid transport, vesicle organization, and autophagy. These findings establish amyloid plaques as conserved, dynamic multicellular hubs that link amyloid accumulation to downstream cellular events.

neuroscience↗

bioRxiv2026

Cell type-resolved proteomics reveals intra- and intercellular signalingin Alzheimers disease

Alzheimers disease (AD) arises from pathological interactions among diverse brain cell types, but cell-specific proteomic changes remain underexplored. Here, we present deep proteomic profiling of sorted or proximity-labeled brain cells from AD mouse models (5xFAD and AppNL-G-F) at multiple ages, quantifying 13,411 proteins in microglia (three subtypes), astrocytes, oligodendrocyte precursor cells, and neurons. We identified 3,028 differentially abundant proteins across these cell types, the majority of which were not detected in bulk proteomic datasets, and constructed cell type-specific networks to define functional modules and hub proteins. Comparison with transcriptomic data revealed that [~]30% of proteomic changes are RNA-independent. Further analyses uncovered cross-cell type signaling proteins conserved in human AD brains, such as pleiotrophin (Ptn), which is transcriptionally enriched in astrocytes but accumulates in microglia. Importantly, recombinant PTN directly activates induced microglia-like (iMG) human cells. Thus, these findings provide a comprehensive cell type-resolved proteomic atlas of AD models, highlighting novel intra- and intercellular signaling events. HighlightsO_LIA high-resolution cell type-resolved proteomic atlas of Alzheimers disease mouse models C_LIO_LI[~]3,000 cell type-specific protein alterations identified beyond bulk tissue analyses C_LIO_LIProteomic profiling of microglial subtypes reveals subtype-specific changes in Alzheimers disease C_LIO_LIAstrocyte-microglia signaling is highlighted and validated through PTN-mediated interactions C_LI

neuroscience↗

bioRxiv2026

Longitudinal Proteomics Analysis of Cerebrospinal Fluid in Survivors of Childhood Acute Lymphoblastic Leukemia

Mass spectrometry-based proteomic profiling of cerebrospinal fluid (CSF) in large patient cohorts, particularly among childhood cancer survivors, remains scarce, limiting opportunities for benchmarking, method development, and validation. Here, we present a longitudinal CSF proteomics dataset from survivors of childhood acute lymphoblastic leukemia (ALL). Samples were collected from 178 patients enrolled in the TOTXVI therapeutic protocol at two timepoints: diagnosis (pre-treatment) and during the consolidation phase of chemotherapy (totaling 356 samples). CSF proteomes were profiled using tandem-mass-tag (TMT) labeling coupled with extensive fractionation and high-resolution liquid chromatography-tandem mass spectrometry (LC/LC-MS/MS). The dataset includes quantitative profiles for more than 3,000 confidently identified unique proteins. This resource enables the investigation of chemotherapy-induced alterations in the CSF proteome and provides a valuable resource for studying the molecular mechanisms of neurotoxicity, identifying biomarkers of adverse late effects, and guiding the development of neuroprotective strategies in childhood cancer survivors.

systems biology↗

bioRxiv2022

Meta-omics-aided isolation of an elusive anaerobic arsenic-methylating soil bacterium

Soil microbiomes harbor unparalleled functional and phylogenetic diversity and are sources of novel metabolisms. However, extracting isolates with a targeted function from complex microbiomes is not straightforward, particularly if the associated phenotype does not lend itself to high-throughput screening. Here, we tackle the methylation of arsenic (As) in anoxic soils. By analogy to mercury, As methylation was proposed to be catalyzed by sulfate-reducing bacteria. However, to date, there are no anaerobic isolates capable of As methylation, whether sulfate-reducing or otherwise. The isolation of such a microorganism has been thwarted by the fact that the anaerobic bacteria harboring a functional arsenite S-adenosylmethionine methyltransferase (ArsM) tested to date did not methylate As in pure culture. Additionally, fortuitous As methylation can result from the release of non-specific methyltransferases upon lysis. Thus, we combined metagenomics, metatranscriptomics, and metaproteomics to identify the microorganisms actively methylating As in anoxic soil-derived microbial cultures. Based on the metagenome-assembled genomes of microorganisms expressing ArsM, we isolated Paraclostridium sp. strain EML, which was confirmed to actively methylate As anaerobically. This work is an example of the application of meta-omics to the isolation of elusive microorganisms.

microbiology↗

bioRxiv2021

Development and Validation of an LC-MS/MS Based Quantitative Assay for Marmoset Insulin

IntroductionInsulin is a peptide hormone that is secreted by the {beta} cells of the pancreas and is essential to the metabolism of carbohydrates, fats, and proteins in the body. The marmoset insulin peptide is not homologous with human insulin and therefore commonly available assays do not work for this species. Due to the increasing popularity of marmoset research, a simple, specific assay for the quantitation of marmoset insulin is needed. This study aimed to develop and validate a bottom-up proteomic workflow with trypsin digestion and analysis using LC coupled with triple quadrupole mass spectrometry (LC-MS/MS). MethodsMarmoset serum proteins were subjected to denaturation, reduction, and enzymatic cleavage to extract a unique, 7 amino acid peptide for quantitation of marmoset insulin.Resolution of the peptide was achieved by LC-MS/MS using electrospray ionization operating in positive mode. Calibration was achieved by aliquot dilution of fully synthetic marmoset insulin tryptic peptide into macaque serum. A stable-isotope labeled (13C, 15N) synthetic marmoset insulin tryptic peptide was used as internal standard. ResultsThe assay was fully validated according to bioanalytical method validation guidelines for linearity, precision, and dilution linearity using purified marmoset insulin. The limit of detection was 4.28 pmol/L and the limit of quantitation was 8.62 pmol/L. Biological validation was achieved by comparison of samples previously run by radioimmunoassay and measurement of the marmoset insulin response to glucose via an oral glucose tolerance test (OGTT). The physiological range of marmoset insulin was shown to be 84.5 to 1222 pmol/L. ConclusionsThis paper presents a simple, reproducible method to measure marmoset insulin using LC-MS/MS.

biochemistry↗