Biology subjects

Manjunath, A.

Publications and source records attributed to Manjunath, A..

4 recordsLinked to original sources

bioRxiv2026

Microscope control with a natural language agent

Microscopy underpins modern biology, but acquiring high-quality data from complex experiments at scale remains challenging. Smart microscopy can standardize and automate data collection, but each experiment-specific solution requires coding expertise and development time. To address this, we present MicroClaw, an AI agent that advises on experiment- and system-specific parameters and approaches and collaboratively plans and executes diverse, complex, and reusable imaging workflows across microscope platforms, without requiring expert knowledge.

biophysics↗

bioRxiv2025

Development of a High-Throughput Ion Mobility Spectrometry-Based Screening Platform for Kainoid Synthase Biocatalysts

Random mutagenesis generates large, diverse enzyme libraries, however high-throughput screening is challenging when transformations yield isomeric small molecule products. We developed a matrix-assisted laser desorption/ionization-trapped ion mobility spectrometry-mass spectrometry (MALDI-TIMS-MS) platform for rapid, label-free screening of libraries of kainoid synthases, Fe/KG-dependent cyclases that produce the isomeric neurotransmitters kainic acid (KA) and kainic acid lactone (KAL). This platform achieves near-baseline separation of KA and KAL directly from microbial biomass, with parallel reaction monitoring-PASEF (PRM-PASEF) enhancing signal-to-noise for isomeric product ratio analysis. Using this workflow, we screened a 318-variant DsKabC/GfKabC DNA shuffle library in under 30 minutes, identifying seven variants with improved KAL conversion. Two variants showed a near-complete shift toward KAL production with greater substrate consumption, while retaining favorable expression profiles compared to GfKabC. These results establish MALDI-TIMS-MS as a generalizable platform for high-throughput isomeric product screening and provides new kainoid synthase variants to probe structure-function relationships for further development as biocatalysts. Importantly, this screening approach can be broadly applied to interrogate enzyme variants acting on small molecules, delivering rapid and accurate quantitative insights without reliance on chromatography, enzyme purification, or substrate derivatization. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/674781v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@185877dorg.highwire.dtl.DTLVardef@18234d6org.highwire.dtl.DTLVardef@15b0df9org.highwire.dtl.DTLVardef@f28f8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Demystifying image-based machine learning: A practical guide to automated analysis of field imagery using modern machine learning tools

Image-based machine learning methods are quickly becoming among the most widely-used forms of data analysis across science, technology, and engineering. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically required a large amount of manual labor. The potential of image-based machine learning methods to change how researchers study the ocean has been demonstrated through a diverse range of recent applications. However, despite their promise, machine learning tools are still under-exploited in many domains including species and environmental monitoring, biodiversity surveys, fisheries abundance and size estimation, rare event and species detection, the study of wild animal behavior, and citizen science. Our objective in this article is to provide an approachable, application-oriented guide to help researchers apply image-based machine learning methods effectively to their own research problems. Using a case study, we describe how to prepare data, train and deploy models, and avoid common pitfalls that can cause models to underperform. Importantly, we discuss how to diagnose problems that can cause poor model performance on new imagery to build robust tools that can vastly accelerate data acquisition in the marine realm. Code to perform our analyses is provided at https://github.com/heinsense2/AIO_CaseStudy

ecology↗

bioRxiv2019

Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell RNA-sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation without prior knowledge has remained challenging. Here we describe a simple yet robust determinant of developmental potential--the number of detectably expressed genes per cell-- and leverage this measure of transcriptional diversity to develop a new framework for predicting ordered differentiation states from scRNA-seq data. When evaluated on ~150,000 single-cell transcriptomes spanning 53 lineages and five species, our approach, called CytoTRACE, outperformed previous methods and ~19,000 molecular signatures for resolving experimentally-confirmed developmental trajectories. In addition, it enabled unbiased identification of tissue-resident stem cells, including cells with long-term regenerative potential. When used to analyze human breast tumors, we discovered candidate genes associated with less-differentiated luminal progenitor cells and validated GULP1 as a novel gene involved in tumorigenesis. Our study establishes a key RNA-based correlate of developmental potential and provides a new platform for robust delineation of cellular hierarchies (https://cytotrace.stanford.edu).

genomics↗